Certified Forward Deployed Engineer

•The FDE paradigm: role, responsibilities, and real-world scenarios

•Comparison: FDE vs. ML Engineer vs. Solutions Architect vs. Consultant

•Systems thinking: input → processing → output mental models

•Business-to-technical translation frameworks

•Problem decomposition techniques (first-principles, MECE)

•Understanding constraints, trade-offs, and feasibility analysis

•Identifying signal vs. noise in client requirements

•Writing clean, modular, and testable Python functions

•Project structuring: separation of concerns, configuration management

•Type hints, docstrings, and code documentation

•Working with JSON, environment variables, and API responses

•Error handling, logging, and defensive programming

•Virtual environments and dependency management (pip, poetry)

•Code quality tools: linting (ruff), formatting (black), testing (pytest)

•API fundamentals: REST, HTTP methods, status codes, request-response flow

•Designing JSON-based endpoints with clear contracts

•Building APIs with FastAPI (routes, dependencies, async)

•Pydantic models for request/response validation

•Authentication basics (API keys, JWT)

•Error handling, exception management, and API testing

•Auto-generated documentation (Swagger/OpenAPI)

•Microservices fundamentals and inter-service communication

•How data moves through modern systems

•ETL vs. ELT: principles and trade-offs

•Pipeline design patterns: batch vs. streaming

•Storage strategies: in-memory, file-based, JSON, SQL, vector DBs

•Database design fundamentals (relational + NoSQL)

•Database design fundamentals (relational + NoSQL)

•Connecting backend logic with persistent storage

•Introduction to data observability

•ML fundamentals: supervised, unsupervised, reinforcement learning

•Feature engineering and data preprocessing

•Model selection: when to use which algorithm

•Training, validation, and evaluation strategies

•Cross-validation, hyperparameter tuning

•Classification and regression metrics

•Experiment tracking (MLflow, Weights & Biases)

•Model versioning, registry, and reproducibility

•ML pipelines: training → evaluation → deployment

•When to use ML vs. LLM vs. rule-based systems

•Neural networks: perceptrons, activations, backpropagation

•CNNs for vision tasks (high-level intuition)

•Transfer learning and pre-trained models

•NLP fundamentals: tokenization, embeddings, attention

•Transformer architecture (conceptual)

•HuggingFace ecosystem for practical use

•LLM-based NLP systems: classification, extraction, summarization

•Integrating DL models into application backends

•Prompt engineering fundamentals: clarity, context, constraints

•Prompt structuring patterns: zero-shot, few-shot, chain-of-thought

•Role prompting, system prompts, and structured output (JSON mode)

•Advanced techniques: self-consistency, ReAct, reflection

•Managing variability and non-determinism in LLM outputs

•LLM workflow integration: chaining, routing, fallbacks

•Practical LLM evaluation: rubrics, LLM-as-judge, human-in-the-loop

•LLM workflow integration: chaining, routing, fallbacks

•Cost, latency, and token optimization

•Why LLMs need external context

•The RAG flow: chunk → embed → retrieve → generate

•Embedding models and vector databases (FAISS, Chroma, Pinecone)

•Chunking strategies and metadata design

•Importance of retrieval quality and context windows

•Common RAG failure modes and mitigations

•Hybrid retrieval, re-ranking, and query rewriting

•Evaluating RAG systems

•What is an agent? Reasoning + tool use

•Tool calling / function calling patterns

•Multi-step agents and planning loops

•Frameworks overview: LangChain, LlamaIndex, custom orchestration

•Memory: short-term, long-term, episodic

•Multi-agent collaboration patterns

•Reliability, guardrails, and safety

•When NOT to use agents

•Why UI matters in client engagements

•Streamlit fundamentals: layout, widgets, state

•Connecting UI with backend APIs

•Gradio as an alternative for ML demos

•UX patterns for AI applications (streaming, retries, transparency)

•Polishing prototypes for client presentations

•Git workflows for team collaboration

•Docker fundamentals: images, containers, Dockerfiles

•Containerizing AI applications

•Cloud architecture basics (AWS / Azure / GCP)

•Deploying APIs to the cloud (managed services, container registries)

•Real-time monitoring, logging, and observability

•Security in production: secrets management, API rate limiting, compliance basics

•Performance optimization at scale (caching, batching, async)

•CI/CD foundations for AI applications

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•Structuring technical demos for impact

•The narrative arc: problem → insight → solution → impact

•Handling live demos under uncertainty

•Visualizing AI outputs effectively

•Anticipating and handling tough questions

•Post-demo follow-up and momentum management