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.