AWS Bedrock GenAI Lead Developer

Job Description - AWS Bedrock GenAI Lead Developer

About the Role:

We are seeking an experienced AWS Bedrock GenAI Developer to lead the design, build, and optimize generative AI applications leveraging Amazon Bedrock, foundation models (FMs), and retrieval-augmented generation (RAG) pipelines. The ideal candidate will have strong expertise in cloud-native development, Amazon Agent Framework (AAF), StreAmlined Agents (Strend Agents), and the AWS Bedrock Python SDK for production-grade GenAI solutions.

Key Responsibilities:

• Develop and deploy GenAI applications using Amazon Bedrock and the Bedrock Python SDK.
• Build and orchestrate AI agents using the Amazon Agent Framework (AAF) and Strend Agents for enterprise automation.
• Design retrieval-augmented generation (RAG) workflows, integrating Bedrock with vector databases (Amazon OpenSearch, Pinecone, Qdrant, or Weaviate).
• Implement agent-based workflows with memory, tool integration, and multi-agent collaboration.
• Apply prompt engineering, fine-tuning, and evaluation techniques to improve reliability, grounding, and response accuracy.
• Integrate Bedrock agents with enterprise APIs, structured/unstructured data sources, and SaaS platforms.
• Leverage AWS services (Lambda, Step Functions, DynamoDB, S3, API Gateway, SageMaker, Kendra) to build scalable and secure GenAI solutions.
• Work with LangChain, LangGraph, or Semantic Kernel alongside Bedrock agents when required.
• Ensure best practices in security, governance, and compliance for AI workloads on AWS.
• Collaborate with product teams and architects to design agent-driven enterprise applications.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Engineering, or related field.
• 3+ years of experience in AI/ML or cloud-native application development.
• Strong experience with AWS Bedrock and Bedrock Python SDK.
• Hands-on expertise with Amazon Agent Framework (AAF) and Strend Agents.
• Proficiency in Python (primary), plus working knowledge of Java or Node.js.
• Experience with RAG techniques, embeddings, and vector search.
• Strong understanding of LLMs, foundation models, and AI agent design.
• Familiarity with LangChain, Semantic Kernel, or workflow orchestration frameworks.
• Solid knowledge of AWS cloud architecture, serverless, and security best practices.

Preferred Skills:

• Experience with fine-tuning or custom FM training on SageMaker.
• Knowledge of multi-agent architectures for domain-specific solutions.
• Familiarity with enterprise connectors (Salesforce, Snowflake, JIRA, ServiceNow).
• Experience implementing CI/CD pipelines for AI and agent applications.
• AWS Certification: AWS Certified Machine Learning - Specialty or Solutions Architect - Professional.