Responsibilities Lead the design, development, and deployment of machine learning models and LLM-based applications. Translate business challenges into scalable AI solutions and define success metrics aligned with business KPIs. Design and implement RAG pipelines, embedding strategies, and vector search architectures. Build agentic workflows, prompt strategies, and orchestration patterns for LLM systems. Own AI/ML solutions end to end, from scoping and design through implementation, deployment, and operationalization, with a high degree of autonomy. Evaluate model and LLM performance using automated and human-in-the-loop methods while implementing guardrails and monitoring. Optimize AI systems for latency, cost, scalability, and reliability. Architect and maintain scalable deployment strategies, CI/CD pipelines, and MLOps workflows. Implement monitoring, drift detection, retraining pipelines, and model lifecycle management practices. Design and maintain production-grade data pipelines ensuring validation, lineage, and reproducibility. Mentor team members, influence AI architecture decisions, and promote responsible AI governance. Stay current with advancements in AI/ML, including LLMs, agentic systems, tooling, and applied best practices, and integrate relevant innovations into team solutions. Qualifications Relevant degree preferred. Advanced degree in Computer Science, Engineering, Data Science, or a related field is a plus. 5 or more years of relevant experience required. Experience in ML engineering, applied AI, or related fields preferred. Experience deploying machine learning models into production environments required. Experience building and deploying LLM-powered applications such as RAG systems, Agentic workflows required. Strong Python expertise and production-grade software engineering practices required. Experience with model serving frameworks and API development (e.g., FastAPI, MLflow, etc.) required. Experience with vector databases and embedding workflows required. Familiarity with orchestration frameworks such as LangChain, LlamaIndex, or similar tools required. Experience with CI/CD pipelines, containerization, and cloud-based deployment environments required. Strong understanding of evaluation methodologies for both predictive ML and LLM systems required. Experience building AI/ML systems in startup, high-growth, or large-scale enterprise environments, with a track record of applying learned best practices to improve team standards and delivery maturity, preferred. Experience designing AI platforms or reusable AI templates preferred. Familiarity with model monitoring frameworks and evaluation tooling for LLM systems preferred. Experience working in regulated or high-compliance environments preferred. Experience optimizing cost and performance for large-scale inference workloads preferred. Experience fine-tuning or adapting foundation models preferred.