AI/ML Engineer
Summary
AI/ML Engineer designing and deploying end-to-end LLM and RAG pipelines, MLOps workflows, and data integration systems on AWS (SageMaker, Bedrock, Lambda, EKS) for enterprise clients in regulated industries.
Position: AI/ML Engineer
Location: Chennai - Remote
Shift Timing: 3.00PM - 12.00AM IST
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 multi-modal 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
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)