LLM Engineer

Responsibilities and Qualifications

• Hands‑on, daily use of AI‑assisted and agentic coding tools (e.g., Claude Code, Cursor, Git Hub Copilot, autonomous coding agents) to write and refactor code, automate workflows, and optimize engineering processes.
• Strong experience with Python, particularly in building REST APIs using frameworks like FastAPI.
• Grounding in NLP and machine learning as they relate to building LLM systems
• Strong experience working with key LLM models APIs (e.g. OpenAI, Anthropic)
• Experience building, deploying, and securing MCP servers at scale.
• Understanding of multi‑agent systems and their applications in complex problem‑solving scenarios.
• Designing and implementing RAG systems end to end: vector databases, semantic search, retrieval quality, and chunking strategy.
• Experience with prompt writing for various use cases
• Experience with generative solutions released to prod, at scale, beyond POCs
• Proficiency with server‑side events, event‑driven architectures, and messaging systems.
• Strong critical thinking and systems thinking skills, with experience debugging, optimizing, and making sound engineering decisions across complex backend systems, not just solving isolated problems.
• Solid understanding of security best practices for backend systems, including authentication and data protection.

• 2+ years of experience developing and experimenting with LLMs
• 8+ years of experience developing APIs with Python

• Experience with LLM guardrails
• Experience with LLM Frameworks (e.g. Lang Chain, Llama Index)
• Experience with LLM monitoring and observability
• Experience developing AI/ML technologies within large and business critical applications
• Building evaluation into LLM systems: eval harnesses, regression suites, LLM‑as‑judge, and offline/online quality metrics.