Job Title:  Senior Consultant | AWS | Bengaluru | Engineering | Hybrid Cloud Engineering

Sr Team Lead | Engineering, AI & Data – Engineering | AWS Engineer

   Location:  Bengaluru 

The team

Engineering helps empower and drive mission-critical solutions whether we need to modernize existing systems or implement new technology products and platforms. Through innovation, we improve financial performance, accelerate new digital businesses and fuel growth. Learn more about Engineering, AI and Data

Your work profile

• 6-8 years of overall experience in software engineering, cloudengineering, AI/ML engineering, or related areas.
• Strong production experience with both AWS and Azure
• Strong hands-on serverless development and architecture experience.
• Experience delivering production-grade AI/GenAI applications
• Experience designing event-driven and distributed cloudarchitectures.
• Strong troubleshooting, system-design, and technical leadershipcapabilities. AI & Generative AI Amazon, Bedrock and/or Azure OpenAI.
• Azure AI Foundry and Azure AI Search.
• Amazon SageMaker and/or Azure Machine Learning.
• LLMs, embeddings, vector search, RAG, prompt engineering, andAI agents.
• Integration of LLM applications with
• Lambda/Azure Functions
• Serverless RAG and event-driven GenAI workflows.
• LLM evaluation, observability, guardrails, and responsible AI.
• Programming & Development
• Strong Python programming skills.
• Experience developing REST APIs using FastAPI, Flask, orequivalent.
• Strong knowledge of JSON, REST, authentication, OAuth/OIDC,and API integration.
• Experience with asynchronous programming and distributedsystems.
• Strong understanding of software engineering principles, designpatterns, and clean code.
• Cloud, DevOps & MLOps
• Strong hands-on experience with AWS and Azure

Key skills required: 

• Education-Bachelors in Engineering
• Docker and Kubernetes experience.
• Terraform, AWS CDK/SAM, or Azure Bicep.
• CI/CD using GitHub Actions, Azure DevOps, Jenkins, GitLab, orequivalent.
• MLOps/LLMOps using MLflow, SageMaker, Azure ML, orequivalent.
• Monitoring and observability using CloudWatch, Azure Monitor,Application Insights, OpenTelemetry, or equivalent.
• Security
• AWS IAM and Azure RBAC/Entra ID.
• Secrets management using AWS Secrets Manager and Azure KeyVault.
• Encryption at rest and in transit.
• VPC/VNet, private endpoints, security groups, and networkisolation.
• API authentication, authorization, throttling, and secure APIdesign.
• Understanding of enterprise AI security and data privacy.