Engineering Foundations
You cannot be an FDE without being a strong engineer first. This phase is non-negotiable. Build production-grade coding muscle, not just scripting ability.
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Python (Production Grade)
OOP, async, type hints
Error handling, logging, testing
Clean, readable, maintainable code
REST API consumption & building
Advanced SQL (CTEs, window fns)
PostgreSQL schema design
Pandas, data wrangling
Basic ETL pipelines
AWS or GCP fundamentals
Docker (build, run, compose)
Git, CI/CD basics
Linux CLI confidence
Python 3.11+
PostgreSQL
Docker
FastAPI
pytest
AWS EC2/S3
Git
Exit Criteria
Ship a working REST API with auth, connected to a DB, deployed on cloud
Write a Python script that pulls, transforms, and loads external data end-to-end
Containerise and deploy any project with Docker + CI pipeline
AI & LLM Core Stack
The technical heart of the AI FDE role. You need to understand how LLMs actually work, not just call APIs. This is where most engineers stop — go deeper.
Transformers, attention, tokenisation
Prompt engineering (system, few-shot)
Context windows, temperature, sampling
OpenAI, Anthropic, Gemini APIs
Vector embeddings & similarity search
Chunking strategies & metadata
Hybrid search (dense + sparse)
Production RAG pipelines
LangChain / LlamaIndex basics
Tool use & function calling
Memory patterns (buffer, summary)
Multi-step chains
OpenAI SDK
Anthropic SDK
LangChain
LlamaIndex
Pinecone
Weaviate
ChromaDB
HuggingFace
Exit Criteria
Build a working RAG pipeline over real documents (PDFs, CSVs, SQL) with hybrid search
Build a multi-turn chatbot with tool use (web search, DB query, calculator)
Explain to a non-technical person exactly how your RAG system retrieves and generates answers
Agentic Systems (The 2026 Non-Negotiable)
This is what separates a demo from a deployment in 2026. Enterprises aren't buying chatbots anymore — they're buying autonomous agents that execute real workflows. You need to build these and make them reliable.
ReAct, Plan-and-Execute patterns
Tool/function calling design
Agent state & memory management
Human-in-the-loop workflows
LangGraph state machines
CrewAI multi-agent orchestration
Agent-to-agent communication
Supervisor + worker patterns
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Reliability & Guardrails
Input/output validation
Fallback & retry strategies
Hallucination detection
Rate limiting, cost controls
LangGraph
CrewAI
AutoGen
Pydantic AI
MCP
Instructor
Guardrails AI
Exit Criteria
Build a multi-agent workflow that autonomously executes a 5+ step real-world task
Implement human-in-the-loop approval at a critical decision point
Your agent recovers gracefully from tool failures, bad model outputs, and rate limits
Evals, Observability & LLMOps
This is the single biggest gap between junior and senior AI engineers. An FDE's job is to prove to a Fortune 500 security team that your agent won't go rogue in production. You cannot do that without evals.
LLM-as-judge patterns
RAGAS for RAG evaluation
PromptFoo for prompt testing
Custom domain-specific evals
Tracing (LangSmith, Braintrust)
Token usage & cost monitoring
Latency & throughput tracking
Prompt regression detection
Model versioning & rollback
A/B testing prompts & models
Caching strategies (semantic)
Fine-tuning fundamentals
LangSmith
Braintrust
HoneyHive
PromptFoo
RAGAS
Weights & Biases
Helicone
Exit Criteria
Build an eval suite for a RAG or agent system with at least 3 metric dimensions
Set up full tracing so you can replay any production failure and find the root cause
Run a prompt regression test that catches a model update breaking your pipeline
Enterprise Integration & Deployment
Most AI projects fail not because the model is bad, but because it can't talk to the customer's legacy SQL databases or meet their data residency requirements. This is the "integration wall" — and crossing it is what earns FDE compensation.
SSO, SAML, OIDC / OAuth2
Legacy ETL & data warehouse patterns
Enterprise API patterns (SOAP, GraphQL)
Data residency & compliance (GDPR)
PII detection & redaction
Role-based access control
Air-gapped & on-prem deployments
SOC2, HIPAA, FINRA basics
System design for scale
Async queues (Celery, RabbitMQ)
Kubernetes basics
Microservices & API gateway
Kubernetes
Terraform
Airflow
Kafka
dbt
Snowflake
Auth0
Vault
Exit Criteria
Deploy an AI system that integrates with at least 3 external enterprise systems (DB, auth, API)
Implement PII redaction before data hits the LLM
Document your architecture well enough that a client's security team can approve it
Client Skills & The FDE Mindset
The hardest part to teach. You need to disagree with a VP and a customer in the same week and keep both relationships intact. This is what makes FDE compensation different from a regular engineer.
Translating vague asks into specs
Identifying the real problem (not stated)
Estimating complexity for non-technical stakeholders
Saying no strategically
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Stakeholder Communication
Exec-level presentation (no jargon)
Status updates that build trust
Writing crisp technical docs
Managing scope creep firmly
Radical ownership mentality
Ship under ambiguity & pressure
Context-switch between clients
Build-measure-learn in the field
Notion
Loom
Linear
Miro
Retool
Postman
Confluence
Exit Criteria — You're Ready
You've shipped at least one AI system for a real external user (not just yourself)
You can demo a technical system to a non-technical executive in under 10 minutes
You can estimate effort, flag risks, and propose a phased plan on a discovery call
You have a GitHub portfolio of 3+ production-grade AI projects with READMEs