AI Lead/Developer

dotsolved

Chennai, India 8 Years Exp Posted 56d ago

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

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.

 

Must-Have Skills & Responsibilities:

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)

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