We are seeking a Generative AI Developer to design, build, deploy, and operate AIpowered applications natively on AWS. The ideal candidate has strong experience in Python, handson expertise with AWS Bedrock (Agent Core SDK), AWS Strands SDK, and a solid foundation in cloudnative development, DevOps pipelines, and observability. Work closely with platform, data, and product teams to deliver secure, scalable, and productiongrade GenAI solutions.
About the Role
Generative AI Developer (AWSNative)
Location: Offshore (Hyderabad/Noida/Pune/Bangalore/Chennai)
Experience: 68 years (with handson GenAI experience preferred)
Responsibilities
- Generative AI Development
- Design and implement Generative AI applications using AWS Bedrock, including:
- Bedrock Agent Core SDK
- Foundation Models (FM) integration
- Prompt engineering and agent orchestration
- Design and develop cloudnative applications on AWS using:
- AWS Lambda, ECS/EKS, EC2
- API Gateway / Application Load Balancer
- S3, DynamoDB, Aurora, OpenSearch
- Build AI workflows using AWS Strands SDK for scalable model execution and orchestration
- Develop and maintain reusable AI components, APIs, and services in Python
- Optimize model performance, latency, and cost for production workloads
- Automate build, test, deployment, and rollbacks for GenAI workloads
- Security, Governance & Compliance
- Ensure secure handling of data used in AI workflows
- Implement:
- Encryption at rest and in transit
- Secure secrets management (AWS Secrets Manager / Parameter Store)
- Follow enterprise standards for:
- Data privacy
- AI governance
- Responsible AI usage
Qualifications
Technical Skills (Must Have):
- Python (advanced proficiency)
- Handson experience with:
- AWS Bedrock
- AWS Bedrock Agent Core SDK
- AWS Strands SDK
- Strong knowledge of AWS services and cloudnative design patterns
- Experience with:
- LLMs, RAG (Retrieval Augmented Generation)
- Vector databases and embeddings
- Experience building and deploying applications natively on AWS
- Familiarity with MLOps or Model Lifecycle Management
- Experience with cost optimization for AI workloads
- Understanding of ethical AI and responsible AI principles
- CI/CD pipeline implementation and maintenance
- Observability and monitoring in production environments
