Senior AI Engineer

Quick Facts

• Role: Senior AI Engineer

• Focus: End-to-end development and operations of production AI systems

Description

Build and run production AI applications and reusable services, owning solutions from problem framing and integration through deployment, monitoring, and support. You will create LLM workflows, tool-enabled agents, multimodal applications, and RAG pipelines that turn messy engineering and business inputs into grounded AI capabilities. You will also apply classical ML (e.g., classification, anomaly detection, computer vision, audio processing) and help shape technical direction.

Responsibilities

• Own AI applications and services end-to-end: prototyping, integration, production deployment, monitoring, and support

• Build agents, multimodal applications, LLM workflows, and RAG pipelines (prompt design, tool calling, structured outputs, embeddings, hybrid search)

• Ingest and parse unstructured or messy inputs (datasheets, schematics, test logs, media, business data)

• Integrate AI with engineering tools, manufacturing/test systems, and business applications via APIs and MCP servers with validation, security, and human oversight

• Deploy and monitor classical ML models alongside LLM systems

• Create repeatable evaluations to improve accuracy, reliability, latency, and cost

• Develop and operate Python services on Azure using PostgreSQL, Terraform, containers, CI/CD, and observability

• Propose architectures, define reusable patterns, evaluate new models and tooling, and mentor engineers through code/design reviews

Requirements

• 5+ years of software engineering experience delivering and supporting production LLM applications beyond proof-of-concept

• Strong Python and backend skills (API design, testing, debugging, maintainable software)

• Hands-on experience with LLM APIs, prompt design, and tool-enabled agents (LangChain, LangGraph, or equivalents)

• Practical RAG experience: embeddings, vector/hybrid search, reranking, and evaluation of retrieval/answer quality

• Solid ML fundamentals: data processing, feature engineering, model training and evaluation; production shipping of non-LLM models (PyTorch, scikit-learn, NumPy, pandas)

• Cloud deployment experience, preferably Azure, with containers, CI/CD, infrastructure as code, and monitoring

• Strong SQL skills, preferably PostgreSQL; understanding of authentication, access control, and secure data handling

• Clear communication of technical trade-offs; ability to contribute independently and mentor others

• Bachelor’s or Master’s in Computer Science/Engineering or related field, or equivalent experience

Benefits

• Hands-on ownership of production AI systems end-to-end

• Opportunity to shape technical direction and reusable patterns within the AI team