LLM / GenAI Engineer
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Key skills for this role
Role Overview
The LLM / GenAI Engineer builds production AI systems combining foundation models, retrieval, structured data, and reliable software services.
The role moves GenAI systems from prototype to production while balancing answer quality, latency, cost, security, observability, and failure recovery.
Key Skills for This Role
Full Job Posting
Role Overview
The LLM / GenAI Engineer builds production AI systems combining foundation models, retrieval, structured data, and reliable software services.
The role moves GenAI systems from prototype to production while balancing answer quality, latency, cost, security, observability, and failure recovery.
Responsibilities
- Design and deploy RAG pipelines using Python, LangChain, LlamaIndex, or custom orchestration frameworks.
- Build secure agentic workflows connecting LLMs to APIs, databases, search systems, and business tools.
- Develop evaluation systems using benchmark data, golden responses, LLM judges, human review, and regression testing.
- Fine-tune and optimize models using supervised fine-tuning, LoRA or QLoRA, prompt optimization, distillation, and model routing.
- Implement production services with Python, FastAPI, Docker, and cloud infrastructure.
- Integrate commercial and open-source model providers.
- Monitor token usage, model quality, hallucinations, latency, failures, and data drift with tracing, metrics, and alerts.
- Establish controls for PII, prompt injection, data access, model versioning, and safe deployment rollbacks.
Required Qualifications
- Three to eight years of software engineering, machine learning engineering, or applied AI experience, including at least one year delivering LLM or GenAI systems to production.
- Strong Python and backend service experience, including asynchronous workflows, REST APIs, automated tests, and CI/CD.
- Hands-on experience with RAG, embedding models, vector databases such as Pinecone, Weaviate, Milvus, Chroma, or pgvector, and hybrid retrieval.
- Knowledge of LLM evaluation, prompt engineering, function calling, structured generation, fine-tuning, and quality, latency, and cost tradeoffs.
- Experience with AWS, GCP, or Azure and containerized deployment using Docker and Kubernetes or an equivalent platform.
- Bachelor's or master's degree in a relevant technical field, or equivalent professional experience.
Preferred Qualifications
- Experience with Llama or Mistral, inference optimization, distributed training, multimodal models, graph-based retrieval, ML observability, responsible AI, or security practices.
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