Head of AI ML Platform Engineering

Company Overview 

Every day, Guardian gives 27 million people the security they deserve through our insurance and wealth management products and services. Since our founding in 1860, our long-term view has helped our customers prepare for whatever life brings whether starting a family, planning for the future, or taking care of employees. Today we’re a Fortune 250 company and a leading provider of life, disability and other benefits for individuals, at the workplace and through government sponsored programs.

Do you have the desire to be part of this collaborative & innovative technology group? Is a culture where “People Count” and “We do the Right Thing” and “We Hold Ourselves to Very High Standards” important to you?

The Role

The Head of AI Engineering and Operations will lead the development, deployment, and ongoing management of enterprise-scale AI solutions, including generative AI models, AI agent frameworks, and robust AI platforms. This role combines technical mastery with strategic leadership to embed AI into underwriting, claims processing, customer engagement, and risk management, while fostering a culture of innovation, operational excellence, and ethical AI practices.

Key Responsibilities

Strategic AI & Platform Leadership

• Define and execute the enterprise AI roadmap, prioritizing generative AI (LLMs, RAG pipelines), AI agent ecosystems, and AI-augmented decision-making.
• Architect, build, and evolve scalable, secure, and efficient AI/ML platform engineering to support a broad range of AI workloads, including generative AI, traditional ML, and autonomous agents
• Oversee the automation, deployment, scaling, and management of AI platform services, ensuring high availability, performance, and resilience
• Lead cross-functional teams of AI platform engineers, ML engineers, and decision engineers to deliver robust, production-ready AI solutions
• Collaborate with C-suite and business leaders to align AI and platform initiatives with business goals, such as reducing claims processing time or improving risk prediction accuracy.

AI Engineering & Operations

• Oversee the build and maintenance of cloud-native AI/ML platforms (AWS) with MLOps pipelines (Kubeflow, MLflow) and monitoring tools
• Implement and maintain CI/CD pipelines for AI model deployment, versioning, and rollback
• Ensure security, compliance, and responsible AI practices throughout the AI platform lifecycle, including bias detection, explainability, and adherence to insurance regulations (e.g., NYDFS, HIPAA)
• Monitor and troubleshoot platform issues to ensure seamless, uninterrupted AI operations

Innovation & Future-Readiness

• Pilot and scale emerging technologies:
• Generative AI workflows (automated document generation, synthetic data creation).
• AI agents for real-time customer interactions and claims triage.
• Collaborative "vibe coding" environments for rapid AI prototyping and innovation.
• Multimodal AI (voice + text) for empathetic customer interactions.
• Evaluate and integrate new AI technologies and tools, driving continuous improvement and operational excellence

People Leadership & Culture

• Build, lead, and mentor high-performing, diverse teams of AI engineers and platform specialists, fostering a culture of innovation and continuous learning
• Evangelize AI adoption and platform best practices across actuarial, customer service, and compliance teams.
• Manage vendor partnerships and internal talent development for AI platforms and technologies.

Qualifications

• Experience: 15+ years in AI/ML engineering, including 5+ years in leadership roles at financial services or insurance firms.
• AI Platform Expertise:
• Proven success in architecting, deploying, and scaling enterprise AI/ML platforms in regulated industries
• Deep knowledge of cloud-native AI/ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML), MLOps frameworks, and AI infrastructure automation
• Experience with generative AI (LangChain, Hugging Face), AI agent frameworks (AutoGen, CrewAI), and vector databases.
• Technical Skills: Fluency in Python, PyTorch/TensorFlow, and containerization/orchestration tools (Docker, Kubernetes).
• Leadership: Track record of building and leading large, cross-functional teams of platform engineers, ML engineers, and data scientists
• Education: Advanced degree in Computer Science, Data Science, or related field.
• Other: Strong communication, business acumen, and ability to translate complex technical concepts for executive audiences.

Salary Rang