Artificial Intelligence Engineer - RAG/LLM

Description :

What youll do Own AI feature delivery from prototype ?

- Build RAG pipelines (chunking, embeddings, vector stores), prompt/program orchestration, and guardrails.

- Fine-tune and/or distill models (open/closed source) for classification, generation, and tool-use.

- Implement robust offline & online evals (unit evals, golden sets, regression tests, user-feedback loops).

- Ship reliable services : APIs, workers, model servers, and monitoring/observability (latency, cost, quality).

- Partner with product/design to shape problem statements, success metrics, and experiment plans.

- Champion engineering best practices (reviews, testing, docs, incident learnings).

Requirements :

- Tech you might use here :

- Languages : Python, TypeScript/Node.

- AI/ML : PyTorch, Hugging Face, OpenAI/Anthropic/other LLM APIs, vLLM/TensorRT-LLM, LangChain/LlamaIndex (pragmatically).

- Data & Retrieval : Postgres, Redis, Milvus/pgvector/Weaviate, Kafka.

- Infra : Docker, Kubernetes, CI/CD, Grafana/Prometheus, cloud (AWS/GCP).

- Quality : Prompt/unit tests, offline eval harnesses, canary analysis, A/B testing.

- Were looking for 3 to 7+ years of software engineering experience, with 13+ in applied ML/LLM or search/retrieval.

- Strong Python engineering (typing, testing, packaging) and service design (APIs, queues, retries, idempotency).

- Hands-on with at least two of : RAG in prod, finetuning (LoRA/QLoRA), embeddings/annoy/hnsw, function/tool calling, or model serving at scale.

- Practical evaluation mindset : create golden datasets, design metrics (accuracy, faithfulness, toxicity, latency, cost).

- Product sense and ownership : you measure impact, not just model scores.

- Clear communication and collaborative habits (PRs, design docs, incident notes).

Nice to have :

- Experience with multi-tenant architectures, RBAC/ABAC, and data governance.

- Safety & reliability work (red-teaming, jailbreak defenses, PII handling).

- Frontend familiarity (React) to iterate quickly on UX for AI features.

- Prior startup experience or 0?1 product building.

What success looks like (first 90 days) :

- Ship a scoped AI feature into customer hands with an eval harness and dashboards.

- Reduce either latency or cost of an existing pipeline by ~2030% without quality loss.

- Add at least one reusable internal component (chunker, ranker, guardrail, eval set).

Interview process :

- Intro chat (30 min) : role fit & expectations.

- Technical deep-dive (60 min) : systems + ML/LLM problem solving.

- Practical exercise (take-home or pairing, 34 hrs) : build a small RAG/eval pipeline.

- Final loop (6090 min) : product & culture, past work, offer Q&A.