Forward Deployed Engineer Course
A 6-month course for engineers who want to build, deploy and support real AI systems using AI apps, RAG, agents, multi-agent workflows, LLMOps and production delivery practices.
Program fee
Contact admissions for current pricing and EMI options.
No published fee on source site — 6-month program; contact admissions for pricing.
Build AI systems that survive contact with real workflows.
The FDE course is structured around a practical transition: from building AI functionality to delivering AI systems that can be explained, validated, monitored and handed off inside business environments.
Scope the business workflow
Learn how an FDE frames the use case, identifies constraints and defines what success looks like in a live environment.
Build the AI system layer
Move through AI apps, RAG pipelines, agent workflows and API-backed delivery patterns that can support real usage.
Support production readiness
Add evaluation, tracing, observability and handoff thinking so the system can be discussed, supported and improved after rollout.
A role program for engineers delivering AI systems into real environments
This FDE course is designed for engineers moving toward forward deployed AI engineer, AI solutions engineer, AI implementation engineer, AI deployment engineer and production AI engineer responsibilities.
The program brings together application building, agentic workflow design and production AI operations into one learning arc shaped around delivery, handoff and continuous improvement.
Business fit Translate workflows and user requirements into AI system plans. System scope Work across LLM apps, RAG, agents and production support layers. Operating model Admissions, fit and program guidance are shared through counseling.
Talk to Our Team View Course Structure
Discover the operational problem
Work close to users, teams or customers to understand the workflow, friction points and business constraints before choosing an AI pattern.
Design the delivery architecture
Decide when the problem calls for an LLM app, a RAG system, an agent workflow or a more structured orchestration pattern.
Support rollout and iteration
Carry the system into real usage with APIs, feedback loops, evaluation practices and production-readiness decisions.
Why Forward Deployed AI Skills Matter Now
Many teams can create an AI demo. Fewer can scope the business workflow, choose the right architecture, validate quality and support the system after rollout. That is where forward-deployed AI capability becomes valuable.
Understand business workflows
Map decisions, inputs, outputs and operational constraints before choosing the system design.
Build LLM and RAG systems
Turn workflow needs into usable AI applications with retrieval, structured outputs and backend integration.
Create agent workflows
Add tool use, routing and automation when a problem needs coordinated task execution.
Evaluate output quality
Use practical checks for correctness, grounding, consistency and workflow usefulness.
Monitor behavior
Instrument systems with tracing, logs and observability signals once real usage begins.
Explain trade-offs
Communicate cost, latency, reliability and design choices to technical and non-technical stakeholders.
Move prototypes toward production
Prepare APIs, handoff notes, quality signals and improvement loops for post-launch support.
3 + 1.5 + 1.5 Months of Forward Deployed AI Delivery
The journey moves from AI application foundations to agentic workflow design and then into LLMOps, evaluation and production handoff thinking.
One cohort, one capstone arc and one role target.
The program is structured as one forward deployed engineering journey. Each phase adds a new delivery layer without losing the business-facing context established at the start.
Layer 1 Application engineering and backend AI delivery Layer 2 Agentic workflows and automation design Layer 3 Evaluation, observability and production support
The Forward Deployment Layer
The forward deployment layer teaches how to take AI systems into real business environments. It connects engineering with problem discovery, solution design, stakeholder communication, production handoff and continuous improvement.
This is the part of the program that turns technical skill into business-side AI delivery capability.
A forward deployed engineer is expected to understand workflow context, make architecture decisions, communicate trade-offs and support production handoff with clarity.
That is why this course teaches more than model calls or workflow automation in isolation. It teaches how delivery choices get made around real users, teams and operating constraints.
Course Curriculum
Customer-facing AI
Module 1: Python for AI apps
- LLM applications
- APIs and backend integration
- Structured outputs
Module 2: RAG in the field
- Vector databases
- Grounding
- Tool calling
Delivery
Module 3: Agents on-site
- Agent workflows
- Multi-agent orchestration
- Task routing
Module 4: MCP and integration
- MCP patterns
- Handoffs
- Runbooks
Frequently asked questions
Why Forward Deployed AI Skills Matter Now
Many teams can create an AI demo. Fewer can scope the business workflow, choose the right architecture, validate quality and support the system after rollout. That is where forward-deployed AI capability becomes valuable.
What You Will Build
The project sequence is designed to move from AI application development into forward-deployed delivery and production system readiness.
What This Course Covers — and Where Deeper Specialization Begins
The course is deliberately scoped around forward-deployed AI delivery. It gives breadth across application engineering, agentic workflows and production AI support, then points clearly to deeper adjacent tracks when needed.
How This Course Fits into the AI Career Academy Ecosystem
The FDE course sits at the center of a specific learning map: included tracks create the delivery stack, while adjacent tracks offer deeper specialization beyond the scope of this role program.
Ready to start?
Talk to an advisor about this program — 15 minutes, no sales pitch.