Job Description:
We are modernizing our data and analytics ecosystem by embedding AI and Generative AI across core insurance platforms (Policy, Claims, Billing, and Enterprise systems).
We are hiring a Lead AI Engineer to build and scale production-grade AI solutions on AWS. This role is hands-on and focused on delivering real systems, while helping shape the foundation of our emerging AI platform.
This is not a pure research or modeling role. It is an engineering role focused on building, deploying, and operating AI systems in a regulated enterprise environment.
Build AI Systems (Core Responsibility)
Design and implement end-to-end AI/ML solutions including LLM-based applications
Build RAG pipelines using vector databases and enterprise data sources
Build machine learning models that automate their training, validation, monitoring, and retraining
Develop APIs and services to operationalize AI capabilities across the organization
Develop Data + AI Pipelines
Build ingestion for multimodal content and transformation pipelines for structured and unstructured data
Integrate AI workflows with enterprise systems (policy, claims, billing, etc.)
Ensure data quality, traceability, reliability, and governance in all AI pipelines
Operationalize Models (MLOps)
Implement CI/CD for AI/ML workflows
Deploy, monitor, and maintain models in production
Manage model versioning, performance monitoring, and retraining processes
Build on AWS
Develop solutions using: Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related services
Contribute to evolving use of AWS Bedrock
Apply Responsible AI Practices
Implement guardrails for LLM-based systems (grounding, validation, safety)
Ensure secure handling of sensitive data (PII, financial, etc.)
Build systems aligned with enterprise governance and compliance standards
Lead by Doing
Provide technical guidance and mentorship to engineers
Contribute to engineering standards and reusable patterns
Partner with architects and business teams to deliver high-impact use cases
Qualifications:
Required
10+ years in software, data engineering, 5 years AI/ML engineering
Hands-on experience building production AI/ML systems
Experience with RAG pipelines, LLMs, or NLP-based systems
Experience with AWS Bedrock or similar GenAI platforms
Experience with data pipelines and distributed systems
Experience deploying and operating systems in AWS
Working knowledge of MLOps practices (CI/CD, monitoring, versioning)
Preferred
Experience with vector databases (Pinecone, Weaviate, etc.)
Experience in regulated industries (insurance, finance, healthcare)
Exposure to microservices and containerized environments (Docker, Kubernetes)
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