Key Responsibilities

- Design and develop scalable AI/GenAI solutions on AWS and Azure.

- Build and deploy RAG applications, AI agents, LLM-based APIs, and intelligent automation solutions.

- Develop serverless and event-driven AI applications using AWS and Azure native services.

- Integrate LLM applications with AWS Lambda and Azure Functions.

- Design scalable distributed and event-driven cloud architectures.

- Develop production-grade AI services using Python.

- Build REST APIs using FastAPI, Flask, or similar frameworks.

- Implement CI/CD pipelines and Infrastructure-as-Code for cloud workloads.

- Implement monitoring, logging, security, governance, and cost optimization.

- Work with enterprise applications, APIs, databases, and cloud platforms.

- Participate in architecture discussions and provide technical guidance to engineering teams.

Technical Skills

Generative AI &

- LLMs

- Amazon Bedrock and/or Azure OpenAI.

- Azure AI Foundry and Azure AI Search.

- Amazon SageMaker and/or Azure Machine Learning.

- LLMs, embeddings, vector databases/search, and RAG architecture.

- Prompt engineering, AI agents, and agent frameworks.

- LLM evaluation, observability, guardrails, and responsible AI.

- Serverless RAG and event-driven GenAI workflows.

Model Context Protocol &

- AI Agents

- Understanding of Model Context Protocol (MCP) and experience building or configuring MCP servers.

- Experience developing AI agents using Bedrock Agents, Agent Core,



or similar frameworks.

Python &

- Application Development

- Strong Python programming skills.

- Experience with Lambda handlers and boto3.

- Experience with LangChain and/or LlamaIndex.

- REST API development using FastAPI, Flask, or equivalent.

- Knowledge of JSON, REST APIs, authentication, OAuth/OIDC, and API integrations.

- Experience with asynchronous programming and distributed systems.

Cloud, Serverless &

- DevOps

- Strong hands-on experience with AWS and Azure.

- Serverless development using Lambda, API Gateway, S3, DynamoDB, and Azure Functions.

- Docker, ECS, EKS, and/or Kubernetes.

- Infrastructure-as-Code using Terraform, AWS CDK/SAM, or Azure Bicep.

- CI/CD using GitHub Actions, GitLab, Jenkins, Azure DevOps, or equivalent.

- MLOps/LLMOps experience using MLflow, SageMaker, Azure ML, or similar platforms.

- Monitoring and observability using CloudWatch, Azure Monitor, Application Insights, OpenTelemetry, or similar tools.

Additional Requirements

- Minimum 5+ years of hands-on experience in Cloud and GenAI technologies.

- Experience delivering production-grade AI/GenAI applications.

- Robust troubleshooting, system design, and problem-solving skills.

- Experience using AI coding tools such as Amazon Q Developer, GitHub Copilot, Cursor, or Kiro.

- Strong understanding of software engineering principles, design patterns, and clean code.

📌 Cloud AI Engineer (Bengaluru)
🏢 Synectics Apac
📍 Bengaluru