Cloud AI Engineer

About Saarthee: Saarthee is a Global Strategy, Analytics, Technology and AI consulting company, where our passion for helping others fuels our approach and our products and solutions. Our diverse and global team work with one objective in mind: OurCustomers’ Success. At Saarthee, we are passionate about guiding organizations towards insights fueled success. That’s why we call ourselves Saarthee–inspired by the Sanskrit word ‘Saarthi’, which means charioteer, trusted guide, or companion. Cofounded in 2015 by Mrinal Prasad and Shikha Miglani, Saarthee already encompasses all the components of Data Analytics consulting. Saarthee is based out of Philadelphia, USA with offices in UK and India. Position: Cloud AI Engineer Location: Bangalore Work Mode: Hybrid Min-Max Experience: 6-8 years 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 AWS. Strong 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 and/or Azure OpenAI. Experience with Amazon SageMaker and/or Azure Machine Learning. 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, Azure DevOps, Jenkins, GitLab, or equivalent. Knowledge of AWS IAM and Azure RBAC/Entra ID. Experience with AWS Secrets. Understanding of encryption, VPC/VNet, private endpoints, security groups, and network isolation. Strong troubleshooting, system design, analytical, and technical leadership skills. BE/B.Tech or Master's degree in Computer Science or related field.