AI Lead Engineer - AWS Platform

Job Summary

We are modernizing our enterprise data and analytics ecosystem by embedding AI and Generative AI capabilities across Policy, Claims, Billing, and Administrative systems. As the AI Lead Engineer – AWS Platform, you will play a key role in supporting The Mutual Group (TMG), Guide One Insurance, and future members by architecting, designing, and leading the delivery of end-to-end AI/ML and Generative AI solutions on AWS, leveraging Bedrock, Sage Maker, Lambda, Step Functions, Glue, and Vector Databases.

Information Technology

This role is pivotal in shaping our AI platform strategy, establishing engineering standards, and ensuring scalable, secure, and responsible deployment of AI workloads. You will guide a team of engineers in building intelligent, production‑grade systems that improve decision‑making, automate workflows, and enhance customer and agent experiences.

• Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in‑office schedule. The initial training period may require additional in‑office days.

• Lead the design and implementation of a scalable, enterprise‑grade AI platform on AWS, integrating LLMs, Generative AI, and traditional ML models.
• Define architectural standards for LLM orchestration, RAG pipelines, and AI model lifecycle management.
• Design Medallion‑based AI data architecture connecting Policy, Claims, Billing, and Administration systems for unified analytics and AI‑driven insights.
• Partner with enterprise architects to align AI initiatives with cloud modernization, data governance, and security frameworks.
• Evaluate new AWS services (Amazon Q, Bedrock Agents, Titan, Sage Maker Hyper Pod) for platform scalability and business alignment.

• Lead development and fine‑tuning of LLMs, transformers, and generative models using Amazon Sage Maker, Bedrock, or custom frameworks.
• Architect and oversee end‑to‑end MLOps pipelines — from training and validation to deployment, monitoring, and retraining — using Code Pipeline, Sage Maker Model Monitor, and Cloud Watch.
• Implement retrieval‑augmented generation (RAG) workflows integrating Vector DBs (Kendra, Pinecone, or Weaviate) for grounded, domain‑specific AI responses.
• Ensure production‑grade model serving, scaling, and versioning with Sage Maker endpoints, Lambda, and Step Functions orchestration.

• Architect data ingestion pipelines to process multimodal content (PDFs, images, audio, emails, structured/unstructured data) using AWS Glue, Textract, Transcribe, and Comprehend.
• Lead the design of AI‑driven automation workflows for classification, summarization, and entity extraction across insurance documents.
• Optimize pipelines for performance, scalability, and cost efficiency through serverless and event‑driven architectures.

• Define and implement CI/CD practices for AI/ML using AWS Code Pipeline, Code Build, and Terraform/Cloud Formation.
• Standardize infrastructure‑as‑code and environment provisioning across development, staging, and production.
• Integrate monitoring, observability, and audit logging into all AI components to ensure reliability and compliance.
• Drive adoption of containerized model deployments via Sage Maker Jump Start, EKS, or Docker‑based inference endpoints.

• Establish Responsible AI frameworks covering model explainability, fairness, safety, and bias detection.
• Configure Bedrock Guardrails and implement safety layers to prevent hallucinations and enforce ethical responses.
• Ensure compliance with HIPAA, SOC2, and data privacy laws through secure data handling, encryption, and audit trails.
• Partner with Info Sec, Legal, and Risk teams to align AI development with enterprise governance policies.

• Lead a cross‑functional team of AI engineers, MLOps specialists, and data scientists, providing technical direction and mentorship.
• Collaborate closely with business stakeholders, architects, and product teams to identify…