Forward Deployment Engineer (FDE) – Live Training

Forward Deployment Engineer (FDE)  – Live Training

Who can enroll for this course:

Module A1 — The FTE Role and AI in the Enterprise

• What is a Forward-Deployed Engineer (FTE)?
• FTE vs. SWE vs. SA — role boundaries
• Where AI fits in the FTE toolkit
• Course roadmap and learning objectives

Module A2 — Critical Thinking for FDEs

• Separating symptoms from root cause — 5 Whys, Fishbone diagram
• Managing assumptions — recognize and validate before coding
• Asking the right questions — discovery questioning technique
• Trade-off reasoning — reversible vs. irreversible decisions
• Cognitive biases in engineering decisions
• Prioritization under pressure — Eisenhower Matrix

Module A3 — AI and LLM Fundamentals

• What is AI — practical definition
• ML vs. Deep Learning vs. Generative AI
• What is a Large Language Model (LLM)?
• How LLMs are trained — pre-training, fine-tuning, RLHF
• Tokens, context windows, and temperature
• Key LLM providers and models
• Prompt engineering — zero-shot, few-shot, chain-of-thought
• Structured output — JSON mode, function calling

Module A4 — Retrieval-Augmented Generation (RAG)

• RAG architecture and end-to-end workflow
• Embeddings, vector databases, and similarity search
• Document ingestion and chunking strategies
• Retrieval, context construction, and generation
• RAG challenges, improvements, and evaluation

Module A5 — AI Agents

• What is an AI agent — LLM + tools + memory + planning
• Agents vs. chatbots vs. pipelines
• Agent architecture — perception, planning, action, memory
• The ReAct loop — Reason, Act, Observe, Repeat
• Agent frameworks — LangChain, CrewAI, Autogen
• Common agent failures and mitigation

Module A6 — Multi-Agent Systems

• Why multi-agent — when a single agent isn’t enough
• Multi-agent architectures — supervisor, peer-to-peer, pipeline, hierarchical
• Task decomposition and agent specialization
• Inter-agent communication — shared memory, message passing
• Challenges — coordination, conflicts, cost, debugging

Module A7 — Model Context Protocol (MCP) & Integrations

• What is MCP and why it matters for enterprise integration
• MCP architecture — hosts, clients, servers
• MCP primitives — resources, tools, prompts, sampling
• Transport layers — stdio vs. Streamable HTTP
• MCP vs. function calling vs. REST APIs
• Building and invoking an MCP server
• Real-world MCP integrations — filesystem, Git, Slack, databases
• AI workflow and automation patterns

Module A8 — AI Ethics, Safety, and Responsible Deployment

• Hallucinations and factual accuracy
• Bias in LLMs and mitigation
• Data privacy and PII handling
• Guardrails and content filtering
•  Human-in-the-loop for production AI

Section B — System Design :

Module B1 — System Design Fundamentals

• Client-server architecture
• Monolith vs. microservices
• Stateless vs. stateful services
• Synchronous vs. asynchronous communication
• Design principles — SoC, SRP, DRY, KISS

Module B2 — APIs and Communication Patterns

• REST API design — resources, methods, status codes, auth
• Message queues and pub/sub — SQS, Kafka, RabbitMQ
• gRPC and protocol buffers
• WebSockets and real-time communication
• API gateway pattern

Module B3 — Data Storage and Databases

• Relational databases — PostgreSQL, indexing, ACID
• NoSQL databases — MongoDB, DynamoDB, Redis
• Object storage — S3
• Caching strategies — Redis, cache-aside, TTL
• Data modeling — normalization vs. denormalization

Module B4 — Scalability and Reliability

• Horizontal vs. vertical scaling
• Load balancing strategies
• Fault tolerance — retries, circuit breakers, bulkhead, timeouts
• Rate limiting and throttling
• CAP theorem and eventual consistency

Module B5 — System Design for AI Applications

• AI inference service design — batch vs. real-time
• RAG system architecture at scale
• Agent system architecture — tool registry, sessions, cost controls
• MCP servers as microservices
• Event-driven AI pipelines
• Cost optimization — model selection, caching, token budgets

Section C — Devops & Ci/Cd :

Module C1 — Containers and Docker

• Containers vs. virtual machines
• Docker fundamentals — images, containers, layers, registries
• Writing a Dockerfile — FROM, RUN, COPY, CMD, multi-stage builds
• Docker Compose — multi-container local development
• Packaging a Python application

Module C2 — CI/CD Fundamentals

• CI vs. CD vs. CD (integration, delivery, deployment)
• Pipeline anatomy — source, build, test, package, deploy
• GitHub Actions — workflows, jobs, steps, triggers, secrets
• Branch strategies — trunk-based, feature branches
• Environment promotion — dev, staging, production

Module C3 — Deployment and Infrastructure

• Deployment strategies — rolling, blue-green, canary, feature flags
• Cloud deployment — ECS/Fargate, Lambda, EC2, S3
• Infrastructure as Code — Terraform / CloudFormation overview
• Environment management — secrets managers, config management
• Deploying AI applications — MCP servers, RAG pipelines, agents

Section D — Observability :

Module D1 — Observability Fundamentals

• Observability vs. monitoring
• The three pillars — logs, metrics, traces
• Observability culture for FTEs

Module D2 — Logging

• Structured logging — JSON, log levels, correlation IDs
• Python logging — stdlib, structlog
• Log aggregation — CloudWatch, ELK Stack
• Logging best practices — what to log, what not to log

Module D3 — Metrics and Dashboards

• Metric types — counters, gauges, histograms
• RED and USE metrics
• Prometheus + Grafana
• Alerting — symptoms vs. causes, alert fatigue

Module D4 — Observability for AI Systems

• LLM observability — tokens, latency, cost, errors
• RAG pipeline observability — retrieval metrics, quality scores
• Agent observability — tool calls, loop counts, cost per task
• MCP server observability — invocation latency, session health
• LLM observability platforms — LangSmith, Langfuse, Arize Phoenix
• Evaluation drift detection and A/B testing