Senior Gen AI Engineer

What You'll Do

• Design and deploy real-world GenAI applications, including RAG systems, copilots, and agentic workflows.
• Build applications using both commercial APIs (OpenAI, Anthropic Claude, Google Gemini) and open-weight models (Llama, Mistral, DeepSeek).
• Arcitect robust retrieval pipelines using vector databases such as Pinecone, Qdrant, Weaviate, or pgvector.
• Handle advanced prompt engineering, manage structured outputs (JSON mode), and implement function/tool calling.
• Set up rigorous evaluation frameworks (like LLM-as-a-judge or RAGAS) to actively monitor accuracy, hallucinations, latency, and costs.
• Write clean, well-documented code while collaborating with product teams and mentoring junior engineers.

What We’re Looking For

• 4–7 years of overall engineering or data science experience, with at least 1.5+ years deeply focused on building GenAI applications for production.
• Strong proficiency in Python and modern AI libraries like LangChain, LlamaIndex, or Hugging Face.
• Real-world, hands-on experience with RAG pipelines, chunking strategies, embeddings, and hybrid search.
• A solid understanding of LLM quirks-including how to manage context windows and mitigate prompt leakage or drift.
• Experience deploying services on at least one major cloud provider (AWS, Azure, or GCP) using Docker and CI/CD pipelines.

Bonus Points If You Have

• Experience designing multi-agent systems using frameworks like LangGraph, CrewAI, or AutoGen.
• Familiarity with fine-tuning techniques (LoRA, QLoRA) or inference optimization.
• Hands-on experience with the Model Context Protocol (MCP) and agent-to-agent patterns.