{"id":2527569,"url":"https://alion.io/job/saarthee-cloud-ai-engineer","title":"Cloud AI Engineer","company":{"id":3808053,"name":"Saarthee","domain":"saarthee.ai","url":"https://alion.io/company/saarthee-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Boto3","optional":false},{"name":"CI/CD","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Docker","optional":false},{"name":"DynamoDB","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Entra ID","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Terraform","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-09-04T12:29:38Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-18T02:48:58Z","board_verified":true,"closed_at":null,"days_open":43,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":43},"description":"About Saarthee:\nSaarthee 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.\nPosition: Cloud AI Engineer\nLocation: Bangalore\nWork Mode: Hybrid\nMin-Max Experience: 6-8 years\nPosition Summary:\nWe 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.\nYour Role Responsibilities and Duties:\nDesign, develop, and deploy production-grade GenAI and Agentic AI solutions using managed LLM services such as Amazon Bedrock and/or Azure OpenAI.\nWork hands-on with LLM applications involving RAG, AI Agents, prompt engineering, tool calling, and enterprise AI workflows.\nDesign and implement RAG architectures, including document ingestion, chunking strategies, embeddings, vector databases/vector stores, semantic retrieval, reranking, and context management.\nWork with Model Context Protocol (MCP) to enable AI models and agents to securely discover and interact with tools, data sources, APIs, and enterprise systems.\nBuild, configure, integrate, and troubleshoot MCP servers/tools and understand MCP architecture, transports, resources, prompts, and tool invocation.\nDevelop AI Agents and agentic workflows using Amazon Bedrock Agents, AgentCore, LangChain, LlamaIndex, or similar frameworks.\nBuild intelligent workflows involving tool use, function calling, multi-step reasoning, orchestration, memory/context management, and human-in-the-loop processes.\nDevelop scalable Python-based AI applications, including AWS Lambda handlers, boto3 integrations, LangChain/LlamaIndex components, APIs, and automation workflows.\nDesign and implement serverless architectures using AWS Lambda, API Gateway, S3, DynamoDB, Bedrock, EventBridge, SQS/SNS, and related AWS services.\nImplement production-ready prompt engineering and AI guardrails, including prompt templates, system instructions, output validation, hallucination mitigation, content filtering, security, and responsible AI practices.\nEvaluate and optimize LLM applications for accuracy, latency, scalability, reliability, cost, and response quality.\nUse 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.\nCollaborate with product, engineering, cloud, and data teams to translate business requirements into scalable AI solutions.\nContribute to code reviews, technical documentation, architecture decisions, testing, deployment, monitoring, and continuous improvement of AI applications.\nRequired Skills and Qualifications:\n6-8 years of experience in Software Engineering, Cloud Engineering, AI/ML Engineering, or related areas.\nStrong hands-on experience with AWS.\nStrong Python programming and software engineering fundamentals.\nExperience delivering production-grade AI/GenAI applications.\nStrong experience with serverless development and architecture.\nExperience with LLMs, embeddings, vector search, RAG, prompt engineering, and AI agents.\nExperience with Amazon Bedrock and/or Azure OpenAI.\nExperience with Amazon SageMaker and/or Azure Machine Learning.\nStrong understanding of REST APIs, JSON, authentication, OAuth/OIDC, and API integration.\nExperience with asynchronous programming and distributed systems.\nHands-on experience with Docker and Kubernetes.\nExperience with Terraform, AWS CDK/SAM.\nExperience with CI/CD using GitHub Actions, Azure DevOps, Jenkins, GitLab, or equivalent.\nKnowledge of AWS IAM and Azure RBAC/Entra ID.\nExperience with AWS Secrets.\nUnderstanding of encryption, VPC/VNet, private endpoints, security groups, and network isolation.\nStrong troubleshooting, system design, analytical, and technical leadership skills.\nBE/B.Tech or Master's degree in Computer Science or related 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