Position Summary:

We are looking for an experienced Cloud & AI Engineer to design, develop, and deploy scalable AI/GenAI solutions across AWS. The role involves serverless architectures, RAG applications, AI agents, cloud-native services, API development, DevOps/MLOps, security, observability, and enterprise AI integrations.

Your Role Responsibilities and Duties:

- Design, develop, and deploy production-grade GenAI and Agentic AI solutions using managed LLM services such as Amazon Bedrock and/or Azure OpenAI.
- Work hands-on with LLM applications involving RAG, AI Agents, prompt engineering, tool calling, and enterprise AI workflows.
- Design and implement RAG architectures, including document ingestion, chunking strategies, embeddings, vector databases/vector stores, semantic retrieval, reranking, and context management.
- Work with Model Context Protocol (MCP) to enable AI models and agents to securely discover and interact with tools, data sources, APIs, and enterprise systems.
- Build, configure, integrate, and troubleshoot MCP servers/tools and understand MCP architecture, transports, resources, prompts, and tool invocation.
- Develop AI Agents and agentic workflows using Amazon Bedrock Agents, AgentCore, LangChain, LlamaIndex, or similar frameworks.
- Build intelligent workflows involving tool use, function calling, multi-step reasoning, orchestration, memory/context management, and human-in-the-loop processes.
- Develop scalable Python-based AI applications, including AWS Lambda handlers, boto3 integrations, LangChain/LlamaIndex components, APIs, and automation workflows.
- Design and implement serverless architectures using AWS Lambda, API Gateway, S3, DynamoDB, Bedrock, EventBridge, SQS/SNS, and related AWS services.




- Implement production-ready prompt engineering and AI guardrails, including prompt templates, system instructions, output validation, hallucination mitigation, content filtering, security, and responsible AI practices.
- Evaluate and optimize LLM applications for accuracy, latency, scalability, reliability, cost, and response quality.
- Use modern AI coding/developer productivity tools such as Kiro, Amazon Q Developer, GitHub Copilot, Cursor, or equivalent tools as part of day-to-day development.
- Collaborate with product, engineering, cloud, and data teams to translate business requirements into scalable AI solutions.
- Contribute to code reviews, technical documentation, architecture decisions, testing, deployment, monitoring, and continuous improvement of AI applications.

Required Skills and Qualifications:

- 6–8 years of experience in Software Engineering, Cloud Engineering, AI/ML Engineering, or related areas.
- Strong hands-on experience with both AWS
- Solid Python programming and software engineering fundamentals.
- Experience delivering production-grade AI/GenAI applications.
- Strong experience with serverless development and architecture.
- Experience with LLMs, embeddings, vector search, RAG, prompt engineering, and AI agents.
- Experience with Amazon Bedrock
- Strong understanding of REST APIs, JSON, authentication, OAuth/OIDC, and API integration.
- Experience with asynchronous programming and distributed systems.
- Hands-on experience with Docker and Kubernetes.
- Experience with Terraform, AWS CDK/SAM
- Experience with CI/CD using GitHub Actions, Jenkins, GitLab, or equivalent.
- Knowledge of AWS IAM and Azure RBAC/Entra ID.
- Experience with AWS Secrets Manager
- BE/B.Tech or Master's degree in Computer Science or related field.

📌 Cloud AI Consultant (Bengaluru)
🏢 SAARTHEE
📍 Bengaluru