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), GuideOne 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, SageMaker, Lambda, Step Functions, Glue, and Vector Databases.
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.
Responsibilities:
AI Platform Architecture & Strategy
- 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, SageMaker HyperPod) for platform scalability and business alignment.
Model Development, Deployment & Operations
- Lead development and fine-tuning of LLMs, transformers, and generative models using Amazon SageMaker, Bedrock, or custom frameworks.
- Architect and oversee end-to-end MLOps pipelines — from training and validation to deployment, monitoring, and retraining — using CodePipeline, SageMaker Model Monitor, and CloudWatch.
- 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 SageMaker endpoints, Lambda, and Step Functions orchestration.
Intelligent Data Processing & Automation
- 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.
MLOps, DevOps & Infrastructure Automation
- Define and implement CI/CD practices for AI/ML using AWS CodePipeline, CodeBuild, and Terraform/CloudFormation.
- 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 SageMaker JumpStart, EKS, or Docker-based inference endpoints.
Responsible AI, Governance & Security
- 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 InfoSec, Legal, and Risk teams to align AI development with enterprise governance policies.
Leadership, Collaboration & Mentorship
- 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 high-impact AI use cases.
- Drive AI Center of Excellence (CoE) initiatives—develop best practices, reusable components, and internal knowledge repositories.
- Promote a culture of experimentation, continuous learning, and responsible AI adoption across the enterprise.
