Forward Deployed Engineer (FDE) Training Course

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Dur: 4 months
Course fee : 35000 /-

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Forward Deployed Engineer

Course Overview

Great engineering starts with understanding the problem behind the request. Forward Deployed Engineer Training connects software development with customer needs, helping learners approach technical challenges with business context. Explore how requirements become working solutions through problem discovery, application design, data integration, testing, and deployment. Develop the ability to ask useful questions, explain technical decisions, and adapt when project requirements change.

This course brings attention to the complete solution journey, from the first customer conversation to ongoing improvements. Build an engineering mindset that values practical usefulness, dependable delivery, and clear communication alongside code, preparing you to contribute thoughtfully to customer projects.

Description

A customer may know what is slowing their team down without knowing which technology can fix it. Forward Deployed Engineer Training explores how to close that gap. The course brings together requirement analysis, programming, APIs, databases, solution architecture, and deployment fundamentals within a customer focused engineering approach.

Learners examine how to break broad requests into manageable tasks, connect existing systems, troubleshoot integration issues, and communicate progress. Attention also goes to documentation, user feedback, security awareness, and maintaining solutions after delivery. The emphasis is on understanding why a solution is needed, how it should work, and what makes it useful in practice. Develop a structured approach to building software that fits the people, processes, and constraints of a real business environment.

Course Objectives

Forward Deployed Engineer Training aims to help learners connect technical execution with customer understanding. The course objectives are to:

Identify business problems through questions, stakeholder conversations, and observation of existing workflows.
Translate customer requirements into tasks, acceptance criteria, and delivery priorities.
Apply programming, database, and API concepts to design solutions that exchange information across systems.
Evaluate architecture choices against practical considerations such as reliability, security, maintainability, and resources.
Understand testing, deployment, monitoring, and troubleshooting as connected responsibilities throughout the solution lifecycle.
Explain technical concepts in accessible language and communicate decisions to technical and business audiences.
Use documentation and version control to support collaboration, trace changes, and simplify future maintenance.
Assess feedback, identify limitations, and improve solutions without losing sight of the original business need.
Develop ownership by recognizing risks, asking for clarification, and following through on implementation tasks.
Build confidence in presenting solution approaches and discussing engineering tradeoffs with stakeholders.

Prerequisites

- Before starting Forward Deployed Engineer Training, learners should understand basic Python syntax and feel comfortable using a computer terminal.
- You should be able to follow simple programs, work with variables, functions, loops, and collections, and troubleshoot basic coding errors.
- Familiarity with Git and introductory SQL provides a useful foundation for the project work.
- If these areas need strengthening, complete preparatory practice before entering the main curriculum.
- Advanced machine learning mathematics, transformer training, and specialist cloud administration are outside the core entry expectations.
- Plan for approximately 12 to 15 learning hours each week, including instruction, labs, project development, and review.
- Bring a willingness to debug, document decisions, receive feedback, and explain your reasoning throughout practical assignments and simulated customer delivery exercises.

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Course Curriculum

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
Applied lab

Build a typed Python SDK/CLI that consumes a public-style REST interface, handles retries and errors, and ships with tests.

Evidence of learning

Repository with CI, test report, README and a short code-review walkthrough.

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
Applied lab

Build an operations API with PostgreSQL, Redis, migrations, pagination and integration tests.

Evidence of learning

API, database migrations, performance notes and architecture diagram.

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
Applied lab

Add role-aware authentication and a webhook/queue integration to the operations service.

Evidence of learning

Threat notes, auth flow diagram, integration contract and failure/retry tests.

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
Applied lab

Create a web console for the service built in Weeks 1-3 with login, searchable data and an operational action flow.

Evidence of learning

Deployed full-stack application and a 5-minute customer 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
Applied lab

Add an AI-assisted workflow to the full-stack app using structured output and one safe tool call.

Evidence of learning

Validated outputs, cost/latency measurements and failure examples.

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
Applied lab

Build an evaluation harness for the Week 5 workflow and compare two model/configuration choices.

Evidence of learning

Evaluation report with pass/fail thresholds, cost/latency trade-offs and regression suite.

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
Applied lab

Build a knowledge assistant over mixed enterprise documents with citations and user/role filters.

Evidence of learning
Retrieval metrics, grounded-answer eval, access-control tests and index update demo.

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
Applied lab

Extend the operations app with a tool-using workflow that can retrieve context, propose an action and require approval before execution.

Evidence of learning

Trace, state diagram, tool-permission matrix and agent evaluation results.

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
Applied lab

Ingest a messy customer dataset, validate it, normalize it and make it available to the AI workflow.

Evidence of learning

Data-quality report, transformation pipeline and reconciliation checks.

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
Applied lab

Threat-model the flagship solution and implement at least three mitigations in code or infrastructure.

Evidence of learning

Threat model, ADRs, permission model and security review checklist.

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
Applied lab

Deploy the current system to an AWS environment with a database, secrets, network controls and an async queue.

Evidence of learning

Deployment diagram, IAM policy review and cloud-cost estimate.

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
Applied lab

Containerize the application and provision/deploy the AWS stack through Terraform and CI/CD.

Evidence of learning

Reproducible infrastructure, pipeline evidence and rollback procedure.

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
Applied lab

Deploy one service to Kubernetes or a managed lab cluster, instrument the system, and run a controlled incident drill.

Evidence of learning

Dashboard, alert, trace example, incident timeline and runbook.

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
Applied lab

Run a simulated discovery session against an intentionally incomplete customer brief and produce a scoped proposal.

Evidence of learning

Workflow map, problem statement, success metrics, solution architecture, risk register and delivery plan.

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
Applied lab

Teams execute the scoped customer engagement. Mid-week, instructors inject one requirement change and one production/security constraint.

Evidence of learning

Working release candidate, decision log, updated plan, eval report and demo.

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
Applied lab

Ship the flagship deployment, run the production-readiness review and present to a customer-style panel.

Evidence of learning

Live deployment, acceptance report, adoption dashboard, runbook, handoff pack, postmortem and product-feedback memo.

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Who can learn this course

Forward Deployed Engineer Training suits learners with basic Python knowledge who want to connect software engineering with practical AI delivery.
Graduates and final year students can use the pathway to build projects that demonstrate integration, deployment, and technical reasoning.
Backend and full stack developers can explore AI workflows, evaluation, and enterprise security.
Engineers working with cloud platforms or implementation projects can strengthen application development and customer discovery skills.
Aspiring applied AI engineers and solutions engineers can practise turning business requirements into usable systems.
Career switchers may also consider the program after developing the recommended programming foundations.
It is particularly relevant to learners who enjoy investigating problems, communicating with stakeholders, testing assumptions, and taking responsibility for how software works after deployment