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.