Birlasoft
Website:
birlasoft.com
Job details:
Area(s) of responsibility
Job Description (Developer) Generative AI Developer (AWS-Native) Location Hyderabad / Hybrid / Remote (as applicable) Experience 5–10 years (with hands-on GenAI experience preferred) Role Summary We are seeking a Generative AI Developer to design, build, deploy, and operate AI-powered applications natively on AWS. The ideal candidate has strong experience in Python, hands-on expertise with AWS Bedrock (Agent Core SDK), AWS Strands SDK, and a solid foundation in cloud-native development, DevOps pipelines, and observability. You will work closely with platform, data, and product teams to deliver secure, scalable, and production-grade GenAI solutions. Key Responsibilities Generative AI Development
- Design and implement Generative AI applications using AWS Bedrock, including: o Bedrock Agent Core SDK o Foundation Models (FM) integration o Prompt 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 Classification: Internal AWS-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 architectures DevOps & CI/CD
- Design and maintain CI/CD pipelines using tools such as: o AWS CodePipeline / CodeBuild / CodeDeploy o GitHub Actions / GitLab CI (as applicable)
- Infrastructure as Code (IaC) using: o 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: o Amazon CloudWatch (logs, metrics, alarms) o AWS X-Ray tracing o Custom metrics for model behavior and performance
- Monitor: o Model response latency o Token usage and cost o Error rates and failure scenarios
- Participate in incident management, root cause analysis, and system optimization Classification: Internal Security, Governance & Compliance
- Ensure secure handling of data used in AI workflows
- Implement: o Encryption at rest and in transit o Secure secrets management (AWS Secrets Manager / Parameter Store)
- Follow enterprise standards for: o Data privacy o AI governance o Responsible AI usage Required 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 environments Preferred Skills (Good to Have)
- Experience with: o LLMs, RAG (Retrieval Augmented Generation) o Vector databases and embeddings Classification: Internal
- Knowledge of containerization: o 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
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