Principal Machine Learning

Responsibilities

• Establish engineering standards, best practices, and evaluation frameworks for AI systems
• Lead technical decision-making for model selection, system design, and deployment strategies
• Act as the subject matter expert for agentic AI and modern LLM-based systems within the organization
• Architect and deliver production-grade, multi-step AI agents capable of autonomous reasoning, tool orchestration, task decomposition, memory management, and human-in-the-loop escalation—requiring specialized expertise in emerging agentic AI frameworks
• Design and deliver AI systems on enterprise cloud platforms (e.g., AWS, Azure), including LLM services (AWS Bedrock, Azure OpenAI), supporting high-volume, business-critical workflows with strict requirements for reliability, auditability, and performance
• Own the agent evaluation and observability stack, including benchmarking, tracing, regression testing, and performance monitoring
• Optimize LLM inference costs and resource utilization for production workloads
• Partner with business leaders to identify, prioritize, and shape AI-driven initiatives aligned with organizational goals
• Translate complex business problems into scalable AI solutions with measurable impact
• Drive roadmap planning and investment decisions related to AI and automation
• Collaborate with IT, data engineering, and operations teams to integrate AI solutions into enterprise systems
• Mentor and develop machine learning engineers and data scientists
• Provide technical guidance and elevate team capabilities in modern AI practices
• Ensure responsible and compliant use of AI systems, including managing risks related to model behavior, data usage, and regulatory considerations in a highly regulated industry
• Lead evaluation and integration of external AI platforms and vendors, including assessment of cost, intellectual property, scalability, security, and long-term architectural impact

Core Competencies

• Excellent communication skills and ability to explain ML results to non-technical audiences
• Proven ability to operate with a high degree of autonomy and accountability
• Experience driving adoption of AI solutions in enterprise environments
• Ability to influence technical direction and investment decisions across organizational boundaries
• Track record of building engineering culture and raising the technical bar within a team

Qualifications

• Master’s degree (or higher) in Computer Science, Engineering, Statistics, or related quantitative field
• 10+ years of hands-on experience in machine learning, AI, or related disciplines
• 2+ years of recent experience architecting and delivering LLM-based and agentic AI systems in production
• Proven track record of delivering end-to-end AI solutions, from problem definition through production deployment
• Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow)

Preferred Qualifications

• Experience building agentic systems for document-heavy workflows (e.g., claims, underwriting, policy processing)
• Experience with enterprise cloud AI platforms (AWS Bedrock, SageMaker, Azure OpenAI)
• Experience with agent frameworks (LangGraph, LangChain, AutoGen, CrewAI, or equivalent)
• Experience with AI observability and evaluation tools (e.g., Langfuse, LangSmith, or similar)
• Familiarity with Model Context Protocol (MCP) or equivalent tool-integration standards
• Experience deploying AI systems in regulated environments (insurance, finance, healthcare)
• Experience leading AI architecture across multiple teams or domains