Suraj Sharma@suraj_sharma14
If I had 6 months to become an Applied AI Engineer.
I’d do this.
Stage 1: Python + Production APIs FastAPI, async, error handling, webhooks, REST/GraphQL, third-party SDKs.
Stage 2: LLM Fundamentals for Production Tokens, context windows, model routing, embeddings, cost/latency tradeoffs.
Stage 3: Prompt Engineering + Structured Outputs System prompts, few-shot chains, Pydantic/JSON validation, prompt versioning, unit evals.
Stage 4: RAG + Knowledge Grounding Chunking strategies, hybrid search, rerankers, vector DBs, metadata filtering, citation tracking.
Stage 5: AI Workflows + Orchestration Tool calling, state machines, human-in-the-loop, retry logic, fallback chains, session memory.
Stage 6: Build Production-Ready Apps Domain-specific copilots, automation pipelines, streaming UIs, graceful degradation, rate limiting.
Stage 7: Evaluation + Reliability Accuracy scoring, hallucination detection, RAGAS/DeepEval, regression testing, A/B output validation.
Stage 8: AI Infrastructure + Optimization vLLM, Ollama, quantization, KV caching, response streaming, token cost tracking, edge deployment.
Stage 9: Deployment + Observability Docker, CI/CD, cloud hosting, distributed tracing, structured logging, alerting, canary releases.
Stage 10: AI Security + Guardrails Input/output filtering, prompt injection defense, PII redaction, compliance checks, sandboxing.
Stage 11: Open Source + Portfolio Ship end-to-end apps publicly, write architecture docs, record demo walkthroughs, publish eval reports.
Stage 12: Apply Applied AI Engineer, GenAI Developer, AI Integration Engineer, LLM Solutions roles.
Most people stay stuck watching tutorials.
Builders get hired.
