About The Role
The role focuses on building production-grade LLM and generative AI systems - RAG pipelines, agentic workflows, fine-tuning infrastructure, and evaluation frameworks that ship to real users. This is hands-on engineering, not prompt tinkering: the work spans retrieval architecture, model optimization, and serving infrastructure where latency, cost, and reliability all matter.
The position sits on a small, senior AI team with direct influence over architecture decisions and a clear path from prototype to production. Models and pipelines built here power customer-facing product features at scale.
Key Responsibilities
Design and ship production RAG systems - retrieval strategies, chunking, reranking, hybrid search - using LangChain, LlamaIndex, or custom architectures
Build and tune vector database integrations (Pinecone, Weaviate, pgvector) and embedding pipelines for semantic search at scale
Develop agentic workflows and tool-use pipelines with robust error handling, state management, and guardrails
Implement systematic LLM evaluation: benchmark suites, LLM-as-judge pipelines, regression testing, and hallucination detection
Run parameter-efficient fine-tuning (LoRA, QLoRA) and instruction tuning on domain-specific datasets; manage the full training-to-deployment loop
Optimize inference cost and latency through quantization, batching, caching, and model routing across open-weight and frontier APIs
Monitor production LLM systems for drift, cost anomalies, and quality regressions; contribute to team engineering standards and architecture reviews
What We Are Looking For
3-8 years of software engineering or ML engineering experience, including at least 2 years working with LLMs in production
Strong Python skills; comfort with async programming, API design, and cloud infrastructure (AWS, GCP, or Azure)
Hands-on experience with RAG architectures, vector databases, and embedding models in production environments
Practical experience fine-tuning or adapting open-weight models (Llama, Mistral, Qwen) using PEFT methods
Familiarity with LLM observability and evaluation tooling (LangSmith, Langfuse, Ragas, or homegrown equivalents)
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience; track record of shipping AI features end-to-end
Bonus: Experience with Kubernetes-based model serving (vLLM, TGI, Triton), distributed training with PyTorch, or publishing/applied research in NLP