Over 80% of internal enterprise AI demos stall between notebook proof-of-concept (PoC) and production rollout due to hallucinations, 15-second response latencies, permission leaks, or runaway token bills. Organizations searching to hire a freelance AI engineer to take an LLM PoC to production partner with me—Aldian Fazrihady, Senior Freelance AI Application Engineer and Full Stack Programmer.
The 5-Step AI PoC-to-Production Engineering Playbook
- 1. Golden Dataset & Automated CI Evaluation (DeepEval / Ragas): Replacing "vibe checks" with a 100+ scenario benchmark measuring faithfulness, precision/recall, and schema validity on every code change.
- 2. Hybrid Retrieval & Reranking (`pgvector` + BM25 + Cross-Encoders): Fixing retrieval blind spots on exact part numbers, legal clauses, and tabular data.
- 3. Permission-Aware Multi-Tenant Security: Enforcing PostgreSQL Row-Level Security (RLS), PII redaction, and prompt-injection guardrails.
- 4. Sub-Second Streaming & Semantic Caching: Implementing SSE/WebSocket streaming, Anthropic/Gemini prompt caching, and tiered model routing to slash p95 latency and cut token costs by 60–85%.
- 5. Production Observability & Human-in-the-Loop Feedback: Tracing every production run in LangSmith / Arize Phoenix / Langfuse.
Frequently Asked Questions
How long does it take to harden a Jupyter Notebook or LangChain PoC for enterprise launch?
Most AI PoCs can be refactored into a containerized, evaluated, streaming FastAPI + Next.js + PostgreSQL `pgvector` production service within 2 to 4 weeks.
How can we hire you to productionize our AI application?
Contact me on Upwork.