Forward Deployed Engineer Training

About FDE (Forward Deployed Engineer Training)

Forward Deployed Engineer (FDE) Training is a 16-week industry-aligned program designed to prepare engineers to solve real-world customer problems, build AI-enabled software, integrate enterprise systems, deploy solutions to the cloud, and support them in production.

The program goes beyond learning individual technologies. It focuses on the complete journey from understanding a customer problem to designing, building, evaluating, deploying, operating, and handing over a production-ready solution.

Learners develop practical skills across Python, backend engineering, full-stack development, Generative AI, LLM applications, RAG, AI Agents, enterprise data, security, AWS, Docker, CI/CD, Terraform, Kubernetes fundamentals, observability, and customer discovery.

The program is strongly focused on hands-on engineering, real-world problem solving, production readiness, customer collaboration, and measurable business outcomes.

FDE Course Objectives:

The key objective of this program is to develop engineers who can independently take a problem from discovery to production deployment.
By the end of the course, learners will be able to:

• Understand and analyze ambiguous customer requirements
• Translate business problems into technically sound solutions
• Build production-quality Python applications and backend services
• Develop and consume reliable REST APIs
• Work with PostgreSQL, Redis, authentication, authorization, and enterprise integrations
• Build practical full-stack applications using TypeScript, React, and Next.js
• Develop LLM-powered applications using structured outputs and tool calling
• Build and evaluate RAG-based enterprise AI applications
• Design practical AI Agent and Agentic AI workflows
• Work with MCP for tool and context interoperability
• Build data ingestion and data-quality pipelines
• Apply security, authorization, governance, and threat-modeling concepts to AI systems
• Deploy applications on AWS
• Use Docker, GitHub Actions, Terraform, and Kubernetes fundamentals
• Implement logging, monitoring, tracing, SLOs, and incident-response practices
• Conduct customer discovery and workflow mapping
• Define success metrics and acceptance criteria
• Handle changing requirements and production constraints
• Demonstrate, deploy, operate, and hand over production-ready solutions

These objectives reflect the program’s graduate profile and end-state competency described in the syllabus.

Prerequisites:

• Basic knowledge of Python programming
• Basic understanding of programming concepts
• Comfort using a computer terminal/command line
• Basic understanding of SQL
• Basic familiarity with Git is helpful

Who Can Learn This course?

This FDE course is suitable for:

• Software Developers
• Python Developers
• Backend Developers
• Full Stack Developers
• AI/ML Professionals
• Generative AI & LLM Developers
• Data Engineers
• Cloud & DevOps Engineers
• Solutions Engineers
• Engineering Graduates
• Professionals looking to move into AI Engineering
• Developers interested in Forward Deployed Engineering
• Professionals who want to build and deploy real-world AI solutions

The program is also aligned with multiple career paths including Forward Deployed Engineer, Forward Deployed Software Engineer, Implementation Engineer, Applied AI Engineer, AI/LLM Application Engineer, Solutions Engineer, Customer Engineer, AI Solutions Architect, Backend Engineer, and AI Platform Engineer.

FDE Course Content Overview

Production Python and Engineering Discipline

Outcome: Write Python another engineer can safely maintain and extend.

• Type hints, dataclasses/Pydantic models, exceptions and defensive programming
• Project structure, virtual environments, packaging and dependency management
• pytest, fixtures, mocking and test design
• Async I/O fundamentals and when not to use async
• Logging, configuration, environment variables and debugging
• Git workflow, pull requests, code review and practical complexity/DSA refresh

Backend APIs, SQL and Caching

• FastAPI, request/response models and REST conventions
• PostgreSQL schema design, joins, transactions and indexes
• SQLAlchemy, Alembic migrations and safe schema evolution
• Redis caching, cache invalidation and TTL trade-offs
• OpenAPI documentation and integration testing
• Performance basics: N+1 queries, pagination and connection pools

Identity, Authorization and Enterprise Integration

Outcome: Integrate safely with external systems and design APIs that survive real-world failure modes.

• OAuth2/OIDC concepts, JWT validation, RBAC and service accounts
• SSO/SAML awareness and identity-provider integration boundaries
• Webhooks, idempotency keys, retry/backoff and dead-letter patterns
• Rate limits, pagination, API versioning and contract testing
• Secrets management and credential rotation concepts
• Queues/events: when asynchronous integration is the right architecture

Practical Frontend for Forward-Deployed Engineers

Outcome: Build enough frontend to put a professional, usable workflow in front of a customer.

• TypeScript essentials for Python engineers
• React/Next.js component and state fundamentals
• Forms, tables, validation and API integration
• Authentication/session handling
• Loading, error, empty and retry states
• Streaming AI responses and basic deployment

LLM Application Engineering

Outcome: Turn a foundation model into a controlled software component rather than a chat demo.

• Model capabilities, context windows, tokens and embeddings at an engineering level
• System/context design, structured outputs and schema validation
• Function/tool calling and tool contract design
• Streaming, retries, timeouts and provider abstraction
• Cost, latency and model-selection trade-offs
• Failure modes: hallucination, instruction conflicts and brittle outputs

Evaluation, Guardrails and AI Reliability

Outcome: Measure AI quality before production and detect regressions after changes.

• Golden datasets and representative test cases
• Task-level metrics, rubric-based evaluation and human review
• LLM-as-judge: useful patterns, bias and calibration limits
• Prompt/model regression testing and versioning
• Tracing, latency, token usage and cost observability
• Failure taxonomy, adversarial tests and launch thresholds
• Guardrails: input/output validation and bounded tool permissions

Enterprise RAG and Search

Outcome: Build grounded answers over private data with permissions and measurable retrieval quality.

• Ingestion, parsing and document metadata
• Chunking by structure and retrieval objective
• Embeddings and pgvector
• Keyword + semantic hybrid search and re-ranking
• Citations, grounding and answer abstention
• Retrieval-time authorization and tenant filters
• Incremental indexing and delete/update handling
• Retrieval and end-to-end RAG evaluation

Agentic Workflows and MCP

Outcome: Use agents only where dynamic reasoning and tool use create more value than a deterministic workflow.

• Deterministic workflow vs. agent: selection criteria
• Tool schemas, preconditions, postconditions and safe retries
• State, checkpoints and bounded memory
• LangGraph as the primary orchestration framework
• MCP clients/servers and secure tool exposure
• Human approval for consequential actions
• Sandboxing, timeouts and rollback
• Multi-agent systems as an advanced pattern, not the default

Data Engineering for Customer Environments

Outcome: Turn imperfect operational data into trustworthy application inputs.

• ETL vs. ELT; batch vs. streaming
• Schema contracts, data quality checks and reconciliation
• CSV/JSON/Parquet and object-storage patterns
• Incremental loads and change-data-capture concepts
• Queues/event streams and ordering/idempotency concerns
• Lineage and auditability concepts
• Serving operational vs. analytical data

Enterprise AI Security and Architecture

Outcome: Design a production AI system that respects identity, data boundaries and operational risk.

• Tenant isolation and authorization boundaries
• PII/secrets handling, retention and deletion concepts
• Prompt injection and indirect prompt injection
• Tool authorization, least privilege and blast radius
• Audit logs and traceability
• Threat modeling for AI workflows
• Architecture Decision Records (ADRs) and non-functional requirements
• Governance and human-review points for high-risk actions

Linux, Networking and AWS Deployment

Outcome: Deploy an application into a real cloud network and debug the basics without depending on a platform team.

• Linux processes, permissions, system logs and SSH
• TCP/IP, DNS, HTTP/TLS and reverse proxies
• Cloud primitives: compute, storage, databases, identity, networking and queues
• AWS implementation: EC2 or container runtime, S3, RDS, IAM, VPC, Secrets Manager, SQS
• Security groups, private/public networking and least privilege
• Cloud-cost awareness and architecture trade-offs
• Azure/GCP mapping exercise for portability

Containers, CI/CD and Infrastructure as Code

Outcome: Make deployments repeatable, reviewable and safe.

• Docker image design, multi-stage builds and runtime security
• Local composition for multi-service systems
• GitHub Actions for test/build/deploy pipelines
• Terraform resources, modules, variables, state and environment separation
• Secrets in delivery pipelines
• Database migration strategy during deployment
• Blue-green/canary concepts and rollback plans

Kubernetes Fundamentals, Observability and Incident Response

Outcome: Operate what you ship and know where to look when it breaks.

• Kubernetes mental model: pods, deployments, services, ingress, config/secrets
• Health probes, requests/limits and autoscaling concepts
• EKS overview; when Kubernetes is justified and when it is not
• Metrics, logs and traces; OpenTelemetry concepts
• Prometheus/Grafana fundamentals and structured application logs
• SLIs/SLOs, alerting and error budgets
• Incident triage, postmortems and runbooks

Customer Discovery, Workflow Mapping and Solution Scoping

Outcome: Turn a vague customer request into a precise, measurable delivery plan.

• Stakeholder interviews and observation
• Workflow mapping: actors, systems, decisions, exceptions and handoffs
• Problem statement vs. requested feature
• Success metrics, baseline measurement and acceptance criteria
• Requirements, assumptions, constraints and dependencies
• Use-case prioritization and value/risk trade-offs
• Technical scoping, sequencing and effort estimation
• Executive-friendly solution narrative and demo planning

Forward Deployment Sprint

Outcome: Build under ambiguity, changing requirements and real delivery constraints.

• Prioritize a thin vertical slice that proves value quickly
• Integrate customer data, identity and APIs
• Maintain evals while changing prompts/models/workflows
• Run security and architecture checkpoints
• Manage scope, communicate blockers and record decisions
• Prepare production telemetry and support procedures
• Demo incrementally and incorporate user feedback

Go-Live, Adoption, Handoff and Product Feedback

Outcome: Prove that the deployment works in the customer workflow and can be operated after the project team steps away.

• Go-live checklist and production-readiness review
• User acceptance testing and acceptance criteria
• Adoption and workflow-impact metrics
• Runbook, support boundaries and escalation path
• Incident drill and rollback validation
• Architecture handoff and operational documentation
• Executive demo: outcome, evidence, risks and next steps
• Post-engagement feedback: what should become reusable product capability

Projects and Flagship Capstone