Lead AI Engineer

This is a remote position.

Job Description:

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
  

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)
  

• Experience with vector databases (Pinecone, Weaviate, etc.)
  
• Experience in regulated industries (insurance, finance, healthcare)
  
• Exposure to microservices and containerized environments (Docker, Kubernetes)
  

Requirements

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)
  

• Experience with vector databases (Pinecone, Weaviate, etc.)
  
• Experience in regulated industries (insurance, finance, healthcare)
  
• Exposure to microservices and containerized environments (Docker, Kubernetes)
  

Benefits