Applied AI Engineer

2+, dedicated experience in practical application of GenAI solutions in an enterprise business environment. Designing and operating GenAI orchestration frameworks in production beyond vendor examples (e.g., LangChain systems),

5+ years of strong front-to-back engineering experience, focusing on AI ML platforms and workflows (Python or Java).

Proven experience building and operating production‑grade GenAI / LLM platforms, applying patterns such as RAG, tool/function calling, agentic workflows, and validated structured outputs.

Strong LLMOps expertise, including evaluation harnesses, prompt and version management, regression testing, observability, and reliability measurement in production systems.

Hands‑on experience building AI-first data ingestion pipelines with measurable quality, accuracy, and reliability.

Advanced retrieval experience advanced vector search, including multi‑vector and late‑interaction approaches (e.g., ColBERT, chunking), multi‑stage retrieval pipelines, metadata filtering, re‑ranking. Solid understanding of evaluation metrics and how they shape practical RAG system design (e.g., recall vs precision, latency vs quality, MRR, NDCG).

Experience operating GenAI systems through real production failures (model regressions, retrieval degradation, prompt drift, data quality issues) and designing mitigation strategies.