Technical Lead

- 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/CrewAIKey Responsibilities Generative AI Development- Design and implement Generative AI applications using AWS Bedrock, including:o Bedrock Agent Core SDK o Foundation 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 workloadsAWS-Native Application Development- Design and develop cloud-native applications on AWS using:o AWS Lambda, ECS/EKS, EC2 o API Gateway / Application Load Balancer o S3, DynamoDB, Aurora, OpenSearch- Implement secure IAM roles and policies aligned with least-privilege principles - Build event-driven and microservices-based architecturesDevOps & CI/CD- Design and maintain CI/CD pipelines using tools such as: - AWS CodePipeline / CodeBuild / CodeDeploy o GitHub Actions / GitLab CI (as applicable) - Infrastructure as Code (IaC) using: - AWS CloudFormation / CDK / Terraform - Automate build, test, deployment, and rollbacks for GenAI workloadsObservability & Operations- Implement end-to-end observability for AI and application workloads: - Amazon CloudWatch (logs, metrics, alarms) o AWS X-Ray tracing o Custom metrics for model behavior and performance - Monitor: - Model response latency o Token usage and cost o Error rates and failure scenarios - Participate in incident management, root cause analysis, and system optimizationSecurity, Governance & Compliance- Ensure secure handling of data used in AI workflows - Implement: - Encryption at rest and in transit o Secure secrets management (AWS Secrets Manager / Parameter Store) - Follow enterprise standards for: - Data privacy o AI governance o Responsible AI usageRequired Skills & Qualifications Technical Skills (Must Have) •Python (advanced proficiency)- Hands-on experience with:o AWS Bedrock o AWS Bedrock Agent Core SDK o AWS 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 environmentsPreferred 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 principlesPlease share resume prannoyk1@ with Details:Current CTC:Expected CTC:Notice period:Total Gen Ai Exp:AWS Bedrock Exp:SDK/ Agen Rock Exp:Python Engineering Exp:

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