Principal Machine Learning
About The Position
At AAA Life, we are building a future-focused team using AI and automation to transform life insurance operations. If you're driven by meaningful work and want to deliver solutions that matter to millions of members, this is your opportunity. We are seeking a Principal Machine Learning Engineer to serve as a technical leader within our Automation and AI organization. This role is accountable for defining and driving AI strategy, architecture, and delivery across multiple high-impact enterprise initiatives. The Principal MLE will lead the development of production-grade AI and agentic systems, ensuring successful deployment of business-critical solutions across Claims, Underwriting, and Member Services. These systems directly impact operational efficiency, decision quality, and customer experience at scale. This role requires deep expertise in modern AI, particularly in designing and deploying autonomous, agentic systems, an emerging and highly specialized area with a limited talent pool. This is a hands-on technical leadership role responsible for delivering enterprise-scale AI solutions where architectural decisions, system reliability, and model behavior have direct and measurable business impact.
Requirements
- 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)
- 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
Nice To Haves
- 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
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
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Principal
