• Design and implement GenAI solutions using AWS Bedrock and Agentcore

• Define architecture for LLM-based applications, including RAG pipelines and agentic workflows

• Develop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation

• Build and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases

• Integrate LLM capabilities into enterprise applications via APIs and backend services

• Design and optimize prompt engineering strategies for accuracy, relevance, and performance

• Work with structured and unstructured data sources to enable knowledge-driven AI applications

• Ensure model evaluation, monitoring, and optimization for latency, cost, and response quality

• Collaborate with application, data, and platform teams for end-to-end solution delivery

• Define best practices for security, governance, and responsible AI usage

• Troubleshoot and resolve issues in production GenAI systems

• Provide technical leadership and mentor team members while remaining hands-on

Must have:

• 8+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.

• Hands-on experience on AWS services. Proven experience using AWS Sagemaker and Bedrock leveraging different types of data sources, Training jobs, real-time and batch applications.

• Design and implement agentic AI architectures using frameworks such as LangChain, Strand Agents etc., enabling autonomous task planning, decision-making, and multi-step reasoning.

• Hands-on experience with Amazon AgentCore for building, deploying, and scaling production-grade agentic AI applications, including agent memory management, tool registry, and observability.

• Architect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.

• Proficiency in working with LLM APIs (e.g., Claude, Nova, and other third-party LLM providers), including API integration,and multi-model orchestration strategies.

• Hands-on experience fine-tuning or optimizing large language models (LLM) 

• Familiarity with LLM tool use, prompt templating and context management.

• Strong expertise in Vector Databases, including indexing strategies, embedding generation, similarity search, and integration with RAG architectures.

• Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.

• Develop and maintain Model Context Protocol (MCP) implementations to manage state, context windows, memory, and prompt orchestration across distributed agent systems.

• Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.

• Experience implementing secure, scalable APIs and integrating with 3rd-party data sources and tools

• Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.

• Should have experience with Deep Learning Concepts - Transformers, BERT, Attention models, tokenization, embeddings.

Nice to have:

• Experience with software development, exposure to frontend backend frameworks and communication protocols

• Experience working on Infrastructure as Code (IaC) and CI/CD pipelines

• Experience with NLP concepts: syntactic/semantic analysis, NER etc.