- We are looking for a Generative AI Tech Lead and Developers to provide technical leadership and architectural direction for building and operating production-grade GenAI solutions on AWS. This role combines hands-on development, solution architecture, team mentorship, and operational ownership.
- Team will own end-to-end delivery of GenAI platforms using Python, AWS Bedrock (Agent Core SDK), AWS Strands SDK, and modern DevOps and observability practices, ensuring scalability, security, reliability, and cost efficiency.
- Solid understanding of LLMs(Anthropic (Claude LLM)), embeddings, prompts, tokens, latency, cost factors
- Practical experience with RAG architectures, vector stores, and grounding strategies
- Ability to select and justify model choices (open-source vs proprietary)
- Experience supporting real-world use cases, not just POCs or demos
- Experience in AWS bedrock platform using Python, Strands SDK Multi-Agents, LangGraph/CrewAI
Key Responsibilities Generative AI Development
- Design and implement Generative AI applications using AWS Bedrock, including:
oBedrock Agent Core SDK oFoundation Models (FM) integration oPrompt engineering and agent orchestration
- 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
AWS-Native Application Development
- Design and develop cloud-native applications on AWS using:
oAWS Lambda, ECS/EKS, EC2 oAPI Gateway / Application Load Balancer oS3, DynamoDB, Aurora, OpenSearch
- Implement secure IAM roles and policies aligned with least-privilege principles
- Build event-driven and microservices-based architectures
DevOps & CI/CD
- Design and maintain CI/CD pipelines using tools such as:
- AWS CodePipeline / CodeBuild / CodeDeploy oGitHub Actions / GitLab CI (as applicable)
- Infrastructure as Code (IaC) using:
- AWS CloudFormation / CDK / Terraform
- Automate build, test, deployment, and rollbacks for GenAI workloads
Observability & Operations
- Implement end-to-end observability for AI and application workloads:
- Amazon CloudWatch (logs, metrics, alarms) oAWS X-Ray tracing oCustom metrics for model behavior and performance
- Monitor:
- Model response latency oToken usage and cost oError rates and failure scenarios
- Participate in incident management, root cause analysis, and system optimization
Security, Governance & Compliance
- Ensure secure handling of data used in AI workflows
- Implement:
- Encryption at rest and in transit oSecure secrets management (AWS Secrets Manager / Parameter Store)
- Follow enterprise standards for:
- Data privacy oAI governance oResponsible AI usage
Required Skills & Qualifications Technical Skills (Must Have) Python (advanced proficiency)
- Hands-on experience with:
oAWS Bedrock oAWS Bedrock Agent Core SDK oAWS Strands SDK
- Strong knowledge of AWS services and cloud-native design patterns
- Experience building and deploying applications natively on AWS
- CI/CD pipeline implementation and maintenance
- Observability and monitoring in production environments
Preferred Skills (Good to Have)
- Experience with:
- LLMs, RAG (Retrieval Augmented Generation)
- Vector databases and embeddings
- Knowledge of containerization:
- Docker, Kubernetes (EKS)
- Familiarity with MLOps or Model Lifecycle Management
- Experience with cost optimization for AI workloads
- Understanding of ethical AI and responsible AI principles
Please share resume [Confidential Information] with Details:
Current CTC:
Expected CTC:
Notice period:
Total Gen Ai Exp:
AWS Bedrock Exp:
SDK/ Agen Rock Exp:
Python Engineering Exp:
