Senior AI Engineer; Full-Stack

We are hiring a Senior AI Engineer who builds production-grade AI products end-to-end. You will design and ship AI agents, Retrieval-Augmented Generation (RAG) systems, and fine-tuned small language models, while also owning the full-stack delivery from React/Vue/Angular frontends through Python/Node backends to AWS, GCP and Azure deployments.

Equally important: you are an AI-adopted engineer. You use Claude Code, Cursor, Codex, and other AI coding assistants as a daily multiplier, and you know how to use them well — managing context, controlling token spend, writing CLAUDE.md / AGENTS.md files, using subagents and MCP servers, and applying evaluation-driven workflows so that AI-generated code is shipped responsibly.

• Design, build and deploy AI agents using Lang Chain, Lang Graph, Llama Index, CrewAI or equivalent frameworks — including multi-agent orchestration, tool use, memory, and planning loops.
• Architect RAG pipelines end-to-end: ingestion, chunking, embedding selection, vector stores (Pinecone / Weaviate / Qdrant / pgvector), hybrid search, re-ranking, query rewriting, and evaluation.
• Fine-tune small and open-source language models (Llama, Mistral, Phi, Gemma, Qwen) using LoRA, QLoRA, PEFT, instruction tuning and DPO — and decide when fine-tuning is the right answer versus prompting or RAG.
• Build full-stack AI applications:
  React/Next.js frontends with streaming UIs (Vercel AI SDK / SSE / Web Sockets), FastAPI or Node backends, and well-designed APIs.
• Own deployment, scaling and observability on AWS (Bedrock, Sage Maker, Lambda, ECS/EKS) and GCP (Vertex AI, Cloud Run, GKE), with Docker, Kubernetes, Terraform and CI/CD.
• Implement LLM observability and evals using Lang Smith, Langfuse, RAGAS, Deep Eval — and treat evaluation as a first-class engineering artifact, not an afterthought.
• Apply AI coding assistants (Claude Code, Cursor, Codex, Windsurf, Copilot) as a daily tool with strong discipline around context management, token efficiency, subagents, hooks, slash commands, and MCP servers.
• Address non-functional requirements: latency budgets, cost/token economics, prompt injection defense, PII handling, OWASP LLM Top 10, rate limiting, semantic caching, and graceful degradation.
• Collaborate with product, design and business stakeholders to translate ambiguous problems into shippable AI solutions, and mentor mid-level engineers on AI engineering practices.

• 4+ years of software engineering and at least 2 years of hands-on production work with LLMs (OpenAI, Anthropic Claude, Gemini, or open-source).
• Strong RAG experience: chunking strategies, embedding models, vector databases, hybrid search, re-ranking, evaluation, and avoiding common failure modes.
• Production experience building AI agents with Lang Chain and Lang Graph (or Llama Index, CrewAI, Auto Gen, Pydantic AI). Comfortable with tool/function calling, structured outputs, agent memory and multi-agent patterns.
• Experience fine-tuning small/open-source models (LoRA, QLoRA, PEFT) and using Hugging Face Transformers, Datasets, Accelerate, and the Hub.
• Strong prompt engineering: system design, few-shot, chain-of-thought, prompt caching, structured output schemas, evaluation of prompts as code.

• Daily, production-grade use of Claude Code, Cursor, or Codex. Understands CLAUDE.md / AGENTS.md, project memory files, slash commands, subagents, hooks, MCP servers, and plan-vs-execute workflows.
• Deliberate token and context management: knows when to use Haiku vs Sonnet vs Opus (and equivalents on other providers), uses prompt caching, batches work, prunes context aggressively.
• Disciplined review of AI-generated code, with tests and evals — never ships unread output.
• Backend:
  Python (FastAPI / Flask) and/or Node.js (Type Script). Solid grasp of async patterns, streaming responses (SSE / Web Sockets/ API).
• Frontend:
  React, Next.js, Type Script, Tailwind CSS. Comfortable building streaming chat UIs and agentic interfaces.
• Databases:
  Postgre
  
  SQL, Redis, at least one vector DB. Familiar with schema design, indexing, and query optimization.

• Latency: streaming, parallel tool calls, model…