LLM / GenAI Engineer
About The Role The role sits at the intersection of LLM engineering and production systems - building the GenAI applications, retrieval pipelines, and evaluation infrastructure that power real products used every day. This is not a research position; it is hands-on engineering work where hallucination rates, latency budgets, and cost per token are all metrics that matter. The team ships fast and iterates hard. Expect to own end-to-end AI features - from architecture and prototyping through deployment, monitoring, and continuous evaluation - working alongside applied scientists, platform engineers, and product teams.
Key Responsibilities
Design and build production RAG pipelines using LangChain, LlamaIndex, or custom architectures, including chunking strategies, reranking, and hybrid retrieval Integrate and optimize vector databases (Pinecone, Weaviate, pgvector, Milvus) for semantic search workloads at scale Develop systematic evaluation frameworks for LLM applications: benchmark suites, LLM-as-judge pipelines, human-in-the-loop review, and regression testing against production traffic Fine-tune and adapt models using parameter-efficient techniques (LoRA, QLoRA) and manage the full training-to-deployment workflow Build agentic workflows and tool-use chains with structured outputs, function calling, and robust error handling Deploy LLM services behind APIs with attention to latency, streaming, caching, rate limiting, and cost optimization Instrument LLM systems for observability - tracing, drift detection, safety guardrails - and drive improvements from monitoring data What We Are Looking For 3–6 years of software or ML engineering experience, including at least 1–2 years building LLM/GenAI applications in production Strong Python engineering skills; comfort with async programming, REST APIs, and building reliable services Deep hands-on experience with at least one LLM orchestration framework (LangChain, LlamaIndex) and modern model APIs (OpenAI, Anthropic, or open-source models via vLLM/TGI) Practical understanding of embeddings, vector search, chunking strategies, and retrieval quality tradeoffs Experience with cloud infrastructure (AWS, GCP, or Azure), Docker, and CI/CD practices for ML services MS in Computer Science or related field, or equivalent practical experience shipping production AI systems Bonus: Experience fine-tuning open-weight models (Llama, Mistral), prompt injection defenses and LLM security, Kubernetes-based model serving, or contributions to open-source LLM tooling