LLM / GenAI Engineer

About The Role The role designs and ships production LLM and GenAI systems: retrieval-augmented generation pipelines, agentic workflows, fine-tuning jobs, and evaluation harnesses that hold up under real enterprise traffic. This is not a prompt-tinkering position. The team builds the core AI platform — model serving infrastructure, evaluation frameworks, and RAG architecture — and this role owns meaningful pieces of it end-to-end, working alongside applied scientists, backend engineers, and product leadership. Key Responsibilities Architect and build RAG pipelines using LangChain, LlamaIndex, or custom retrieval stacks, tuning chunking strategies, hybrid search, and reranking for measurable quality gains Implement and scale vector database integrations (Pinecone, Weaviate, pgvector) serving low-latency semantic search in production Design agentic workflows with tool calling, function execution, and guardrails using frameworks like LangGraph or custom orchestration layers Run fine-tuning workloads (LoRA, QLoRA, DPO) on domain-specific datasets using PyTorch and Hugging Face, managing GPU resources across training runs Build systematic LLM evaluation frameworks: golden datasets, LLM-as-judge pipelines, and regression suites wired into CI so quality regressions are caught before deploy Deploy and serve models via vLLM, Triton, or managed inference endpoints, optimizing for cost, latency, and throughput Own observability for GenAI features: tracing with LangSmith or OpenTelemetry, drift detection, and dashboards that surface failure modes quickly What We Are Looking For 3–6 years of software engineering or ML engineering experience, with at least 2 years shipping LLM-powered systems to production Strong Python engineering skills: async programming, API design, testing, and working comfortably in large codebases Hands-on production experience with RAG architecture, including embedding model selection, vector databases, and retrieval quality tuning Experience fine-tuning or evaluating open-source models (Llama, Mistral, Qwen) and working with provider APIs (OpenAI, Anthropic, Bedrock) Working knowledge of cloud infrastructure (AWS, GCP, or Azure), containerized deployments, and GPU-based serving Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience Bonus: experience with RLHF or preference optimization, contributions to open-source LLM tooling, multimodal model experience, or an MS/PhD in a relevant field