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)

Competências necessárias

Large Language Models (LLMs)Retrieval-Augmented Generation (RAG) pipelinesVector databases (e.g., Pinecone, Weaviate)MLOps (CI/CD for ML, monitoring, model versioning, retraining)AWS (Amazon SageMaker, Lambda, S3, Glue, EKS, Bedrock)Data ingestion and transformation for multi-modal (structured & unstructured) dataAPI and service development to operationalize AI capabilitiesModel deployment, production monitoring, and maintenanceData quality, traceability, reliability and governanceResponsible AI and LLM guardrails (grounding, validation, safety)Secure handling of sensitive data (PII, financial data) and compliance alignmentDistributed systems and scalable data/AI pipelinesContainerization and orchestration (Docker, Kubernetes / EKS)Experience in regulated industries (insurance, finance, healthcare)

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