Job Summary

Implement generative AI models on AWS, identify insights that can be used to drive business decisions. Work closely with multi-functional teams to understand business problems, develop hypotheses, and test those hypotheses with data, collaborating with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals. Optimizing existing generative AI models on AWS for improved performance, scalability, and efficiency. -Ensure data quality and accuracy -Implement NLP techniques and prompt engineering methodologies to enhance the capabilities and efficiency of GenAI models. -Experience working with cloud based platforms (AWS preferred) -Strong problem-solving and analytical skills. Experience on Multi Agent Systems, Multi Agent Orchestration is desirable

AI DevOps & ML Ops: Deep expertise in developing, testing, deploying, and monitoring AI applications and infrastructure using Git and robust CI/CD pipelines.

AWS Agentic Workflows: Experience using AWS Bedrock and Agent Core to build AI agents that can reason and execute tasks.

Strong Coding: Proficiency in Python and Node.js for backend development and API integration.

Technical Stack Requirements

AI & ML: PyTorch, Multi Agent Systems, LangChain, Amazon Bedrock, RAG, Natural Language Processing, LLMops, ML Pipelines, Generative AI: Amazon Bedrock (Agents, Knowledge Bases, Guardrails), Agent Core.

Cloud Platform: AWS (Lambda, API Gateway, S3, IAM, CloudWatch/X-Ray for Monitoring).

Programming Languages: Python (Primary), Node.js.

DevOps & Tools: Git, CI/CD (Deep Experience with GitHub Actions / AWS CodePipeline), Terraform or CloudFormation (IaC), Artifact Management.

Integration Standards: REST APIs, OData, OpenAPI (Swagger) Specifications, JSON.

Key Responsibilities

Implement generative AI models on AWS,



identify insights that can be used to drive business decisions. Work closely with multi-functional teams to understand business problems, develop hypotheses, and test those hypotheses with data, collaborating with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals. Optimizing existing generative AI models on AWS for improved performance, scalability, and efficiency. -Ensure data quality and accuracy -Implement NLP techniques and prompt engineering methodologies to enhance the capabilities and efficiency of GenAI models. -Experience working with cloud based platforms (AWS preferred) -Strong problem-solving and analytical skills. Experience on Multi Agent Systems, Multi Agent Orchestration is desirable

AI DevOps & ML Ops: Deep expertise in developing, testing, deploying, and monitoring AI applications and infrastructure using Git and robust CI/CD pipelines.

AWS Agentic Workflows: Experience using AWS Bedrock and Agent Core to build AI agents that can reason and execute tasks.

Strong Coding: Proficiency in Python and Node.js for backend development and API integration.

Skill Requirements

Technical Stack Requirements

AI & ML: PyTorch, Multi Agent Systems, LangChain, Amazon Bedrock, RAG, Natural Language Processing, LLMops, ML Pipelines, Generative AI: Amazon Bedrock (Agents, Knowledge Bases, Guardrails), Agent Core.

Cloud Platform: AWS (Lambda, API Gateway, S3, IAM, CloudWatch/X-Ray for Monitoring).

Programming Languages: Python (Primary), Node.js.

DevOps & Tools: Git, CI/CD (Deep Experience with GitHub Actions / AWS CodePipeline), Terraform or CloudFormation (IaC), Artifact Management.

Integration Standards: REST APIs, OData, OpenAPI (Swagger) Specifications, JSON.

Must Have Skill

AI Cloud

Valuable to Have Skill

Amazon Bedrock

Other Requirements

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📌 Platform Engineer III (India)
🏢 HCLTech
📍 India