The AI/ML Engineer will design and implement end-to-end AI/ML solutions, including LLM-based applications and RAG pipelines. They are also responsible for operationalizing models through MLOps practices and ensuring data governance across enterprise systems.
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)
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