sr. Ai/ml engineer

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.