SB
Spandana Bala
AI Engineer
Hyderabad · India
Now at · Microsoft
Forward Deployed AI Engineer · Agentic AI · MCP · Enterprise delivery · Enrolling now
Forward Deployed AI Engineer
A customer-embedded, code-first program: own the journey from an ambiguous enterprise problem to an adopted production AI system — run discovery and vision-lock, build agents with the Claude Agent SDK, OpenAI Agents SDK and LangGraph, integrate them into the customer's systems through MCP, and ship with evals, governance evidence and adoption metrics.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Digital Edify centres
Where our Forward Deployed AI Engineer alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What does a Forward Deployed AI Engineer do?
A forward deployed AI engineer embeds with a customer and owns the outcome: turns an ambiguous business problem into a scoped AI mission, builds the agents and integrations, ships them into the customer's production systems, and stays until the value is measured. Most AI courses end at a demo. This program ends only when you have run a live customer-style engagement from discovery to a production agent system — integrated through MCP into enterprise systems, evaluated, governed and adopted — with the evidence to prove it.
The complete delivery chain
Twelve links, one owner — end to end.
01DISCOVER & VISION-LOCK
- •Sponsor, stakeholder & process discovery
- •Use-case scoring, value case & cost model
- •Data, systems & security readiness
- •Guardrails, success metrics & acceptance criteria
02BUILD & INTEGRATE
- •Agents with Claude Agent SDK, OpenAI Agents SDK & LangGraph
- •RAG, memory & context engineering on customer data
- •MCP servers into Salesforce, ServiceNow, SAP & legacy APIs
- •FastAPI services, auth & deployment in the customer's cloud
03PROVE & LAUNCH
- •Eval harnesses, red-teaming & regression suites
- •Governance evidence — EU AI Act, NIST, audit trails
- •Staged rollout, rollback & hypercare
- •Adoption, value dashboards & 90-day review
The forward deployed landscape · 2026
Enterprises stopped buying demos. They hire the people who ship.
The FDE model goes mainstreamRole
Pioneered by Palantir and now standard at Anthropic, OpenAI, Salesforce, ServiceNow and the major consultancies — engineers who embed with customers, own the outcome and bring product feedback home. One of the fastest-growing AI job titles of 2026.
Agent SDKs & frameworksBuild layer
Claude Agent SDK, OpenAI Agents SDK and Responses API, LangGraph, Google ADK — plus vendor platforms such as Agentforce, ServiceNow AI Agent Studio and Microsoft Copilot Studio that FDEs configure and extend.
MCP as the enterprise integration standardProtocol layer
Salesforce Headless Toolkit, ServiceNow MCP server, GitHub, Slack, Snowflake and thousands of servers speak MCP; A2A and agent skills extend it. FDEs build the custom servers that connect agents to legacy systems.
Evals, observability & reliabilityProof layer
Eval datasets, LLM-as-judge, regression suites and tracing — LangSmith, Langfuse, Braintrust, OpenTelemetry. Customers now ask for the eval report before the go-live date.
Governance & regulationTrust layer
EU AI Act obligations phasing in, NIST AI RMF and ISO 42001 adoption, plus vendor trust layers (Salesforce Guardian, ServiceNow AI Control Tower). FDEs produce the evidence pack.
Coding agents in deliveryVelocity layer
Claude Code, Codex and Cursor let a small squad deliver what used to take a team — FDEs direct them, review their output and build the skills and MCP servers that make them safe on customer code.
What this means for your career: forward deployed roles pay a premium because they combine engineering with customer delivery — the differentiator is a live engagement you ran from discovery to adopted production system, with eval and governance evidence, not a portfolio of demos.
Who should join
Built for engineers stepping into customer-embedded AI delivery.
Software engineers (any stack)
AI / ML engineers moving to delivery
Solutions & integration engineers
Platform consultants — Salesforce, ServiceNow, Workday
Data engineers & analysts with code skills
Technical founders & product engineers
Prior experience: some programming experience recommended. The program refreshes Python and API foundations quickly, then spends most of its time on agents, integration, evals and customer delivery.
What you will be able to do
Own the mission — from problem to adopted system.
Run discovery & vision-lockStakeholder interviews, use-case scoring, value cases, guardrails and acceptance criteria.
Engineer production agentsClaude Agent SDK, OpenAI Agents SDK and LangGraph with RAG, memory and tool use.
Integrate enterprise systemsCustom MCP servers, APIs and events into Salesforce, ServiceNow, SAP, Workday and legacy systems.
Evaluate & secureEval harnesses, LLM-as-judge, red-teaming, guardrails and tracing.
Govern & evidenceEU AI Act, NIST AI RMF, vendor trust layers and audit-ready evidence packs.
Launch & drive adoptionDeployment in the customer's cloud, rollback, hypercare, training and value review.
Course curriculum
Twelve sections. 51 modules. Discover → Build → Integrate → Prove → Launch → Adopt.
01
Foundations of Enterprise AI & the FDE Role
What forward deployed engineering is, why enterprises hire for it, and how the 2026 AI stack fits together.
Origins — Palantir to Anthropic, OpenAI, Salesforce, ServiceNow
FDE vs AI engineer vs consultant vs solutions engineer
Mission ownership and customer embedding
Career paths and compensation signals
Frontier and open-weight models
Agent SDKs and vendor agent platforms — Agentforce, AI Agent Studio, Copilot Studio
MCP, A2A and interoperability
Evals, observability and governance layers
Tokens, context, reasoning modes and cost
Non-determinism and its consequences for acceptance criteria
Model selection for enterprise constraints
Data residency and zero-retention options
Python, uv, Git and cloud CLI setup
Claude Code and Codex CLI as delivery tools
Secrets, environments and customer sandboxes
Your first agent in a customer-shaped repo
02
Python, APIs & Cloud Refresher
Enough engineering depth to build and deploy in a customer's environment.
Typing, dataclasses and Pydantic
Async and concurrency for LLM calls
Testing with pytest and mocking
Project structure and packaging
Routes, models and dependency injection
Streaming responses and background jobs
Auth, rate limiting and security
OpenAPI and client generation
SQL and PostgreSQL essentials
pgvector, Qdrant and hybrid storage
Data pipelines and PII handling
Text-to-SQL with guardrails
Docker and compose
AWS, Azure and GCP deployment patterns
GitHub Actions pipelines
Environments, secrets and observability basics
03
Discovery, Scoping & Vision-Lock
The front end of every engagement — where most AI projects are won or lost.
Sponsor, user and IT stakeholder mapping
Interview guides and discovery workshops
Reframing ambiguous asks into problems
Capturing constraints and non-negotiables
Feasibility, value and risk scoring
Cost and consumption modelling
Business case and ROI hypotheses
Prioritising the first mission
Data inventory and quality assessment
Systems of record and integration surfaces
Security, identity and compliance constraints
Readiness report
Vision-lock document and success metrics
Guardrails and escalation paths
Acceptance criteria and eval plan
Sponsor sign-off
04
LLM Engineering & Context Engineering
Working with models reliably on customer data.
Anthropic, OpenAI, Google and Bedrock APIs
Open-weight models on-prem with vLLM
Provider selection for enterprise constraints
Routing and fallbacks
Instruction design and reasoning prompts
JSON schema and Pydantic outputs
Tool calling patterns and error handling
Prompt versioning and caching
What goes in the context window and why
Long context vs retrieval
Memory patterns and summarisation
Cost and latency trade-offs
Documents, images and audio in enterprise workflows
Extraction pipelines
Multimodal evaluation
Common enterprise use cases
05
RAG & Enterprise Knowledge
Ground agents in the customer's knowledge — and prove the retrieval works.
Parsing SharePoint, Confluence, PDFs and databases
Chunking, metadata and permissions
Incremental ingestion
Document intelligence and OCR
Embedding models and vector stores
Hybrid search and reranking
Query rewriting and agentic RAG
Permission-aware retrieval
Connecting to Salesforce Data 360, ServiceNow knowledge and SharePoint
Snowflake, Databricks and warehouse grounding
Citations and grounding verification
Freshness and cache strategy
Retrieval metrics and golden datasets
RAGAS and custom eval frameworks
Iterating on retrieval quality
Reporting to the customer
06
Agent Engineering
Build agents that finish the job — with the frameworks enterprises use.
The agent loop and planning patterns
Single vs multi-agent designs
Human-in-the-loop and approval gates
When not to build an agent
Agent loop, built-in tools and subagents
Hooks, skills and memory
Building research and operations agents
Deployment patterns
Agents, tools, handoffs and guardrails
Sessions and tracing
Building a support agent
Comparison and selection
Graphs, state and persistence
Interrupts and human approval
Long-running and background agents
LangGraph Platform deployment
Salesforce Agentforce and Agent Builder
ServiceNow AI Agent Studio
Microsoft Copilot Studio and Google Agentspace
When to build custom vs configure a platform
Supervisor and swarm patterns
Handoffs and shared state
Failure handling and fallbacks
Business agent patterns — support, operations, sales
07
MCP & Enterprise Integration
Connect agents to the customer's systems — the FDE's core technical skill.
Hosts, clients, servers, tools, resources and prompts
Transports and auth
The MCP ecosystem and registries
Security model
Python and TypeScript SDKs
Wrapping legacy APIs, databases and files as tools
Tool contracts agents understand
Testing and publishing
Salesforce Headless Toolkit and Agentforce actions
ServiceNow MCP server and AI Agent Fabric
Workday and SAP APIs through MCP
Identity, permissions and audit
Event-driven and API-led patterns
Kafka, Platform Events and webhooks
Idempotency, retries and reconciliation
Integration testing
A2A and agent cards
Cross-vendor agent orchestration
Trust boundaries between agents
Governance across boundaries
08
Evals, Guardrails & Security
Prove it works — and prove it is safe — before the customer finds out.
What to measure per use case
Building golden datasets with the customer
Offline vs online evaluation
Evals in CI
Judge design and calibration
Tool-use and task-completion evals
Braintrust, LangSmith, Langfuse and promptfoo
Regression suites
Input and output guardrails
Injection and jailbreak defence
PII and content filtering
Permission boundaries for tools
Attack taxonomies and automated red-teaming
Data exfiltration and tool abuse tests
Findings, fixes and retest
Reporting to security teams
OpenTelemetry for LLM apps
Traces, token accounting and dashboards
Alerting and incident response
Cost budgets and consumption guardrails
09
Governance, Risk & Compliance
Produce the evidence enterprises audit before go-live.
EU AI Act obligations and timelines
NIST AI RMF and ISO 42001
Sector rules — finance, health, public sector
Mapping requirements to controls
Salesforce Einstein Trust Layer and Guardian
ServiceNow AI Control Tower
Microsoft and Google governance tooling
Using them as evidence
GDPR, DPDP and CCPA in AI systems
Data residency and zero-retention
Consent and purpose limitation
Deletion and retention
Model cards, eval reports and audit trails
Risk registers and approvals
Working with security, legal and audit
Presenting governance to leadership
10
Delivery — Sprints, UAT, Launch & Adoption
Ship into the customer's production and stay until the value lands.
Epics, stories and acceptance criteria for agents
Sprint planning and prioritisation
Demo cadence and feedback loops
Managing scope in ambiguous work
UAT design for non-deterministic systems
Acceptance against eval thresholds
Defect triage and retest
Sign-off
Deploying in the customer's cloud and network
Feature flags and staged rollouts
Rollback plans and incident handling
Hypercare model
Training and champions
Adoption metrics and dashboards
90-day value review
Handover to run teams and next-wave backlog
11
Consulting & Communication Skills
The customer-facing half of the role.
Embedding etiquette and stakeholder trust
Navigating IT, security and procurement
Managing expectations and scope
Escalation and conflict
Running discovery and design workshops
Demos that build confidence
Executive summaries and decision papers
Handling hard questions
Estimating AI work honestly
SOWs, assumptions and risks
Bringing customer feedback back to product
Documentation and knowledge transfer
12
Live Deployment Practicum & Career
A real customer-style mission from discovery to production — in a squad, with partner mentors.
Receive an ambiguous enterprise brief
Discovery, scoring and readiness assessment
Vision-lock with a sponsor
Eval plan and guardrails
Agent system with Claude Agent SDK or LangGraph
Custom MCP servers into enterprise systems
RAG on customer-shaped data
Sprint reviews with partner mentors
Eval harness, red-team results and evidence pack
Deployment, rollback and hypercare
Adoption and value dashboard
Public verification URL
Engagement case study and GitHub portfolio
Resume rewrite around shipped outcomes
FDE interview practice — system design, customer scenarios, live coding
Warm introductions to hiring partners
Platform specialisations
Go platform-deep. Two FDE tracks on the enterprise stacks that hire most.
The same forward deployed method — discover, build, integrate, prove, launch — applied to a specific vendor platform, with its agent studio, data layer and governance tooling.
Salesforce · Agentforce 360
12 sections · 50 modules
Salesforce Forward Deployed AI Engineer
Ship Agentforce 360 agents grounded in Data 360, connected through AIforce and MCP, governed by the Trust Layer and Salesforce Guardian — from vision-lock to a customer's production org.
Agent BuilderAgent ScriptData 360Headless Toolkit · MCPTesting CenterAgent Fabric
View Salesforce FDE program →
ServiceNow · AI Platform
12 sections · 48 modules
ServiceNow Forward Deployed AI Engineer
Build Now Assist skills and AI agents in AI Agent Studio, connect them through AI Agent Fabric, MCP and Workflow Data Fabric, and launch under AI Control Tower with evaluation evidence and rollback plans.
AI Agent StudioNow Assist Skill KitAI Agent Fabric · MCPWorkflow Data FabricAI Control Tower
View ServiceNow FDE program →
Tools you'll master
32+ GenAI & agentic tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three customer-style missions, each threaded through the entire curriculum. By the practicum, you have run the whole engagement — discovery to adopted production system.
Hero project
Customer mission: service-desk agents shipped into a live enterprise estate
Take an ambiguous brief from a customer sponsor and own it end to end — discovery and vision-lock, a multi-agent system integrated through custom MCP servers into ServiceNow and Salesforce, evals and governance evidence, then a staged production launch with a 90-day value review.
01Discovery & vision-lock — stakeholder interviews, use-case scoring, data and security readiness, a signed vision-lock with guardrails, success metrics and an eval plan.
02Agent system & enterprise integration — Claude Agent SDK or LangGraph agents with human-in-the-loop, custom MCP servers wrapping ServiceNow, Salesforce and a legacy REST API, permission-aware RAG on customer knowledge.
03Proof & governance — golden-dataset evals, LLM-as-judge and trajectory tests, red-team findings and fixes, an evidence pack mapped to EU AI Act and NIST AI RMF controls.
04Launch & adoption — deployment in the customer's cloud, staged rollout with rollback, hypercare, training and a value dashboard for the 90-day review.
Outcome: Adopted production system
Evidence: Eval + governance pack
Reviewer: Customer sponsor + partner mentor
DiscoveryClaude Agent SDKLangGraphMCPEvalsGovernance
Enterprise
Discovery-to-vision-lock sprint
Run a two-week discovery on a real partner brief — stakeholder interviews, use-case scoring, data and security readiness, value case and cost model — and defend a vision-lock document to a sponsor panel.
DiscoveryUse-case scoringValue caseVision-lock
Integration
Enterprise MCP integration & eval harness
Wrap a legacy system and an enterprise platform — Salesforce or ServiceNow — as custom MCP servers, wire them into an agent, and prove it with a golden-dataset eval suite, red-team report and tracing dashboard.
MCPSalesforceServiceNowEvalsLangSmith
Project
Live deployment practicum with a hiring partner.
Join a squad embedded with a partner team. Take their brief from discovery to a production agent system — MCP integration, evals, governance evidence and adoption — reviewed by the sponsor and a partner mentor, with a public verification URL.
Download the real world project
Full scope, sample deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Squad practicumPartner mentorCareer support included
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Edify · AI Engineering Architect
GenAI · Agentic AI · LangGraph · MCP · A2A · Production RAG
"Production GenAI is where AI engineers earn their keep — LangGraph orchestrating
multi-agent topologies, MCP serving your tools to any client, A2A coordinating across services, and an eval
harness that catches regressions before users do. That's the bar I teach to, every class."
15 yrs
AI ENGINEERING
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Digital Edify and brings 15 years of platform architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led production ML and GenAI rollouts for Fortune-500 banks, telcos, and insurers. Most recently he architected production LangGraph + MCP + A2A systems that replaced traditional case-handling tiers with autonomous multi-agent flows, with full eval and observability harnesses behind them.
His classes get you two things other programs don't give you: a founding architect who's shipped agentic AI from inside the Fortune 500, and a curriculum rewritten every quarter — so when hiring managers ask about MCP server fleets, A2A negotiation, DSPy optimization, or LangSmith eval suites, you've already built it. Holds LangChain Academy badges and the AWS Solutions Architect — ML Specialty; M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Edify · Agent Platform & Eval Lead
LangGraph · MCP · A2A · DSPy · Vector DBs · Evals · Agent Observability
"Shipping a multi-agent system to production is the easy part — keeping it healthy
is the work. MCP fleets that don't drift, A2A handshakes that recover from partial failures, golden
datasets that catch regressions before users do. That's what I teach."
10 yrs
AI ENGINEERING
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Edify, where he leads the Agent Platform and evaluation practice. After 8 years shipping production ML and DevOps pipelines, he stepped into the Chief Technologist seat to wire LangGraph, MCP fleets, and A2A into the way real engineering teams actually run agents — replay-able state, golden-dataset evals, drift monitoring, and cost guardrails that keep multi-agent systems quiet on purpose.
His agent and eval modules are built from real production post-mortems, not slide decks. Expect to leave with working MCP servers, an A2A-coordinated multi-agent topology, a DSPy-optimized RAG service, and an Arize + LangSmith observability stack you can stake an SLA on. Holds the Pragmatic AI Engineer track credential and Azure AI Engineer Associate; ten years at Digital Edify, hands-on, and known for the unglamorous parts of agentic AI that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What AI engineering employers say about Digital Edify grads.
Real feedback from engineering leaders at AI labs and the firms hiring our Forward Deployed AI Engineer graduates.
Digital Edify grads ramp 40% faster on GenAI agent rollouts than typical AI engineering hires. Best GenAI engineering pipeline in India.
Aakash Mehta, Engineering Director, Microsoft
We've onboarded 80+ Digital Edify alumni in 18 months. Lowest ramp time we've seen for multi-agent systems and eval practices.
Anita Sharma, Senior Manager, Deloitte
The Forward Deployed AI Engineer programme is comprehensive — discovery, LangGraph, MCP, evals, governance. Grads arrive ready to ship agents into customer production.
Rahul Bhatt, Solutions Lead, Mphasis
Their LangGraph + DSPy + eval track produces PMs who ship production multi-agent systems on day one. Rare combination of engineering rigor and AI craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Edify apart is the agentic platform layer baked into the AI engineer track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their LangChain Academy + Pragmatic AI Engineer prep is rigorous, and the shipped project — multi-agent system, MCP fleet, eval harness — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Edify's AI engineers ship production multi-agent systems twice as fast in the first 90 days. Our internal engineering metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best GenAI engineering pipeline we've sourced from in India. Their projects are real shipped agent systems, not toy demos.
Karthik Subramanian, Engineering Director, Cognizant
Strong GenAI and agentic engineering foundation. Their AI Engineer grads need almost zero ramp time on enterprise agent platform engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Edify alumni across our GenAI and watsonx engineering teams. Strong fundamentals, sharp on eval and observability.
Sanjay Verma, Talent Director, IBM
multi-agent systems + LangGraph evals is exactly the talent gap we've been struggling to close. Digital Edify is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their AI Engineer track delivers engineers who navigate LangGraph, MCP, and A2A on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Edify graduates for our GenAI engineering practice. Strong on LangGraph, sharp on MCP/A2A, fluent in agent eval.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Edify grads who blend multi-agent systems with Azure OpenAI evals land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Edify · Institute Certificate
Agent‑Ready AI Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a multi-agent system —
LangGraph topology, MCP server fleet, A2A coordination, and an eval harness — evaluated against the LangChain Academy badges, AWS ML Specialty, and Pragmatic AI Engineer credential rubrics.
Manikanta Kona
CEO · Digital Edify
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the AI engineering community and mapped to LangChain Academy
and Pragmatic AI Engineer credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: LangGraph topologies, MCP servers, A2A coordination, eval harnesses,
drift monitoring & cost guardrails. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
Job roles
Roles this program prepares you for.
Forward Deployed AI Engineer
Embed with customers to ship agent systems into production.
Forward Deployed Solutions Engineer (Applied AI)
Own discovery, build and launch for enterprise AI deals.
AI Delivery / Implementation Engineer
Deliver agent programs for consultancies and partners.
Agentic AI Engineer
Design and ship multi-agent systems with MCP tools and evals.
AI Integration / MCP Engineer
Build MCP servers connecting agents to Salesforce, ServiceNow, SAP and legacy systems.
Customer Engineer — AI Platforms
Technical delivery for AI platform vendors and labs.
AI Solutions Architect (Applied)
Design enterprise agent solutions across vendors.
AI Quality & Governance Engineer
Own eval harnesses, red-teaming and compliance evidence.
Technical Program Lead — AI Missions
Run squads delivering AI outcomes for customers.
Head of Forward Deployed Engineering (career path)
Grow toward leading FDE teams and practices.
What employers should see in your portfolio: that you can take an ambiguous enterprise problem to an adopted production system — run discovery and vision-lock, build agents with the Claude Agent SDK or LangGraph, integrate them through custom MCP servers, prove them with evals and governance evidence, launch in the customer's cloud and report the value.
04Job placement support
Your first AI Engineer offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into AI labs and AI-first product orgs. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your multi-agent system, MCP fleet, A2A coordination, eval dashboard, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the AI systems you shipped (multi-agent topologies, MCP fleets, eval harnesses), the partner-org project, and the business outcome. Reviewed by AI engineers who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into AI labs and AI-first product orgs — Microsoft, Anthropic, OpenAI partners, Hugging Face, LangChain, Cohere, Mistral, Databricks, Snowflake, Scale AI, Stripe, Razorpay, Freshworks, Zoho, plus services that staff GenAI teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
AI Engineer alumni
Hundreds of AI engineering careers launched — here are eight.
NV
Naveen Vedala
Senior AI Engineer (Agent Platforms)
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Staff GenAI Engineer
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Principal Engineer (Multi-Agent Systems)
Seattle · United States
Now at · Anthropic
MM
Mujahed Mohammed
LangGraph Backend Lead
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
MCP Server Engineer
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
RAG Engineer
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
AI Evaluation Lead
Hyderabad · India
Now at · Cohere
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Principal Engineer (Multi-Agent Systems) classes running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening AI Engineer classes running on IST and PST. Every online class ships
the same shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Admissions
ENROLLING NOW
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the GenAI / agentic stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior ML experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — mechanical, electrical, BCom, BBA, and self-taught coders. The opening modules cover the GenAI fundamentals, LangGraph patterns, and agent design from scratch. What you do need is consistency and regular practice.
Will I actually ship production agents, or only build toy demos?
You actually ship. Every learner deploys a working multi-agent system on LangGraph with MCP-served tools, A2A coordination between agents, and a real eval harness with golden datasets, drift monitoring, and cost guardrails. The project runs in a partner org — not a notebook.
Which models, frameworks, and protocols will I use?
Models: OpenAI, Anthropic, Gemini, Hugging Face, vLLM, Ollama. Frameworks: LangChain, LangGraph, LangSmith, LlamaIndex, DSPy. Protocols: MCP, A2A. Vector DBs: Pinecone, Chroma, Weaviate, Qdrant, Milvus. Safety & obs: Guardrails, NeMo Guardrails, Arize, MLflow, Weights & Biases.
Will I prep for AIPMM AI Engineer and Pragmatic Principal Engineer (Multi-Agent Systems) certs?
Yes. The curriculum is mapped to the AIPMM AI Engineer track and the Pragmatic Principal Engineer (Multi-Agent Systems) credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
How is the learning workload structured?
The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.
Is placement support really 1:1, and which companies hire AI engineers?
Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. Digital Edify does not guarantee an interview, offer, salary, employer, location, or timeline.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), and a weekend track for working professionals. Every format ships the same shipped project — multi-agent system, MCP fleet, A2A coordination, eval harness — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for learners needing additional support, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
