{"id":1189530,"url":"https://alion.io/job/synechron-ai-generative-ai-engineer-python-llms-rag-ai-agents-azure-openai-aws-bedrock","title":"AI / Generative AI Engineer – Python, LLMs, RAG, AI Agents, Azure OpenAI & AWS Bedrock","company":{"id":5306,"name":"Synechron","domain":"synechron.com","url":"https://alion.io/company/synechron","size_band":"5000+","is_staffing_agency":true,"is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":93,"open_postings":14,"ghost_share":0,"stale_share":0.357,"repost_share":0.214,"time_to_fill_p50_days":17,"computed_at":"2026-09-24T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","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":25000,"max_usd":63000,"period":"year","method":"role_seniority_country_cell","sample_n":10},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Copilot","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitLab","optional":false},{"name":"Hugging Face","optional":false},{"name":"JavaScript","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Transformers","optional":false},{"name":"TypeScript","optional":false}],"status":"live","first_seen_at":"2026-09-21T00:00:00Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-09-25T00:17:01Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Job Summary\nSynechron is seeking an AI / Generative AI Engineer with 6+ years of experience in designing, developing and deploying AI-powered applications. The role will focus on Generative AI, Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), cloud-based AI platforms and production-grade software engineering.\nThe position will work with business stakeholders, solution architects, engineering teams, DevOps and MLOps teams to deliver scalable AI solutions that address business needs, integrate with enterprise applications and operate reliably in production environments.\nThis is a full-time position based in Pune, Bengaluru, Hyderabad or Mumbai, with a hybrid working model. The role contributes to business objectives by accelerating AI adoption, improving automation, enabling intelligent applications and delivering secure, maintainable and measurable AI capabilities.\nSoftware Requirements\nRequired\nPython: Strong hands-on experience in Python development for AI applications, data processing, model integration and API development; experience with the project-supported version.\nGenerative AI and LLMs: Practical experience developing applications using LLMs and foundation models.\nNLP and Transformers: Working knowledge of Natural Language Processing, Transformers, embeddings and prompt engineering.\nRAG: Experience designing and implementing Retrieval-Augmented Generation pipelines.\nVector Databases: Experience with one or more of the following:PineconeChromaDBFAISSWeaviateEquivalent vector database technologies\nLangChain and LangGraph: Hands-on experience building LLM applications, orchestration workflows or AI agents using current project-supported versions.\nAgentic AI Frameworks: Experience developing intelligent AI agents and agentic workflows.\n Model Context Protocol (MCP): Working knowledge or practical experience applying MCP concepts in AI applications.\nOpenAI / Azure OpenAI: Experience integrating and using OpenAI or Azure OpenAI services.\nAWS Bedrock: Experience using AWS Bedrock or equivalent managed foundation-model services.\nHugging Face Ecosystem: Familiarity with relevant models, libraries and tools used for Generative AI development.\nCloud Platforms: Hands-on experience with Azure and/or AWS.\nREST APIs and FastAPI: Experience designing or integrating REST APIs and developing AI services using FastAPI.\nDocker and Kubernetes: Experience containerizing and deploying AI applications and services.\nCI/CD Pipelines: Experience supporting automated build, test and deployment pipelines.\nGit and GitHub/GitLab: Experience with source control, branching, code review and collaborative development.\nSQL and NoSQL Databases: Experience working with structured and unstructured data stores.\nData Pipelines: Experience with data ingestion, preparation and processing pipelines.\nMLOps: Experience with model deployment, monitoring and machine learning lifecycle management.\nAI Guardrails and Responsible AI: Understanding of guardrails, governance, safety, monitoring and responsible use of AI.\nPreferred\nExperience delivering enterprise-scale Generative AI solutions.\nExperience with Copilot solutions, AI agents and multi-agent systems.\nExposure to the BFSI domain.\nExperience with React, Node.js or full-stack development.\nUnderstanding of security, compliance and governance requirements for AI applications.\nExperience with knowledge graphs and semantic search solutions.\nExperience with multimodal AI applications.\nExperience with fine-tuning strategies and LLM evaluation frameworks.\nOverall Responsibilities\nDesign, develop and deploy Generative AI solutions using LLMs and foundation models.\nBuild end-to-end AI applications covering data ingestion, prompt engineering, RAG pipelines, model orchestration and API integration.\nDevelop intelligent AI agents and agentic workflows using LangChain, LangGraph, MCP and other suitable orchestration frameworks.\nImplement AI capabilities using Azure OpenAI, AWS Bedrock, OpenAI APIs and related AI services.\nDesign, configure and manage vector databases and semantic search solutions.\nCreate scalable APIs and microservices that integrate AI capabilities into enterprise applications.\nOptimize LLM performance through prompt engineering, fine-tuning strategies, retrieval optimization and evaluation frameworks.\nEstablish appropriate methods for measuring response quality, relevance, accuracy, latency, reliability and cost.\nImplement AI guardrails, responsible AI practices, monitoring and governance mechanisms.\nCollaborate with DevOps and MLOps teams on deployment, monitoring, model lifecycle management and production support.\nApply software engineering practices including version control, code reviews, automated testing, documentation and maintainable architecture.\nWork with business stakeholders and solution architects to understand requirements and translate them into practical AI solutions.\nAssess technical feasibility, integration dependencies, data requirements, risks and operational considerations.\nStay current with developments in Generative AI, Agentic AI, multimodal AI and LLM ecosystems.\nSupport sustainable AI engineering by considering model efficiency, resource utilization, infrastructure cost, reuse and long-term maintainability.\nDeliver production-grade AI solutions that meet agreed functional, security, scalability, reliability and support expectations.\nTechnical Skills (By Category)\nProgramming Languages\nEssential\nStrong Python development skills.\nAbility to write modular, testable, maintainable and production-ready code.\nAbility to develop AI application logic, data-processing components, API services and integration utilities.\nPreferred\nJavaScript or TypeScript experience for AI application integration or full-stack development.\nNode.js experience for backend services.\nReact experience for developing or integrating AI-enabled user interfaces.\nDatabases and Data Management\nEssential\nExperience with SQL and NoSQL databases.\nExperience designing and supporting data ingestion and processing pipelines.\nUnderstanding of structured, unstructured and semi-structured data.\nExperience with vector databases, embeddings, indexing and similarity search.\nUnderstanding of knowledge graph and semantic search concepts.\nAbility to assess data quality, data access, data lineage and data relevance for AI applications.\nPreferred\nExperience with large-scale data processing architectures.\nExperience integrating knowledge graphs with RAG or semantic search solutions.\nExperience optimizing vector search performance and retrieval quality.\nExperience with data governance and metadata management.\nCloud Technologies\nEssential\nHands-on experience with Azure and/or AWS cloud platforms.\nPractical experience using Azure OpenAI, AWS Bedrock or related cloud AI services.\nUnderstanding of cloud-based deployment, scalability, availability, monitoring and access control.\nAbility to integrate cloud AI services with APIs, databases and enterprise applications.\nPreferred\nExperience designing enterprise-scale AI platforms on cloud infrastructure.\nExperience with cloud-based model monitoring, managed AI services and infrastructure automation.\nExperience optimizing cloud resource usage and AI application costs.\nFrameworks and Libraries\nEssential\nLangChain and LangGraph for LLM application development and orchestration.\nAgentic AI frameworks for AI-agent and agentic workflow development.\nOpenAI and/or Azure OpenAI integration.\nAWS Bedrock integration.\nHugging Face ecosystem.\nFastAPI for AI service and REST API development.\nRAG architectures, prompt engineering, Transformers and embeddings.\nExperience with LLM-based applications and foundation models.\nPreferred\nFrameworks for multi-agent systems and Copilot solutions.\nLibraries and tools for LLM evaluation, model fine-tuning and response-quality measurement.\nFrameworks for multimodal AI applications.\nLibraries for model serving, observability and AI application monitoring.\nDevelopment Tools and Methodologies\nEssential\nGit and GitHub/GitLab for source control and collaborative development.\nDocker for containerizing AI applications and services.\nKubernetes for deploying and managing containerized workloads.\nCI/CD pipelines for automated build, test and deployment activities.\nMLOps practices covering model deployment, monitoring, versioning and lifecycle management.\nAPI-first and microservices-based development.\nCode reviews, technical documentation, automated testing and defect resolution.\nAgile development and collaborative delivery practices.\nPreferred\nExperience with infrastructure-as-code and automated environment provisioning.\nExperience implementing model-performance monitoring, data-drift monitoring, latency tracking and usage monitoring.\nExperience with release governance and production support for AI applications.\nSecurity Protocols\nEssential\nUnderstanding of secure AI application design, API security, authentication and authorization.\nAwareness of data privacy, secure data handling and access-control requirements.\nAbility to implement or support AI guardrails for prompt safety, data protection and response control.\nUnderstanding of responsible AI practices, governance, monitoring and human oversight.\nAbility to identify risks related to prompt injection, data leakage, unauthorized access and unsafe model outputs.\nPreferred\nExperience implementing AI governance and model-risk controls.\nFamiliarity with security and compliance requirements for AI applications in regulated or data-sensitive environments.\nExperience supporting auditability, explainability and traceability of AI outputs and model activity.\nExperience Requirements\nAt least 6 years of experience in software development, artificial intelligence, machine learning, data engineering or a related technical field.\nHands-on experience designing, developing and deploying AI-powered applications.\nStrong practical experience with Generative AI, LLMs, NLP, Transformers, embeddings and prompt engineering.\nExperience implementing RAG architecture and vector database solutions.\nExperience developing AI agents and agentic workflows using LangChain, LangGraph, MCP or similar frameworks.\nExperience with Azure OpenAI, AWS Bedrock, OpenAI APIs or related cloud AI services.\nExperience developing REST APIs and microservices using FastAPI or similar technologies.\nExperience with Docker, Kubernetes, CI/CD pipelines, Git and GitHub/GitLab.\nExperience with SQL and NoSQL databases, data ingestion and data processing pipelines.\nExperience with model deployment, monitoring and MLOps practices.\nExperience delivering enterprise-scale Generative AI solutions.\nExposure to the BFSI domain.\nPreferred: Experience with Copilot solutions, multi-agent systems, multimodal AI or full-stack development.\nPreferred: Experience with AI governance, responsible AI, model monitoring, security and compliance.\nCandidates may qualify through equivalent practical experience in software engineering, machine learning engineering, data engineering, AI platform engineering or Generative AI application development that demonstrates the required capabilities.\nDay-to-Day Activities\nDevelop and enhance Generative AI applications, RAG pipelines, AI agents, prompts, APIs, microservices and supporting data-ingestion components.\nCollaborate with business stakeholders, solution architects, engineers, DevOps and MLOps teams to refine requirements, assess designs and coordinate delivery.\nTest and evaluate model responses, retrieval quality, performance, security, guardrails, reliability and operational readiness.\nMake implementation recommendations within the agreed architecture and governance framework while documenting decisions, risks, dependencies and production-support requirements.\nQualifications\nA bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science or a related field is preferred; equivalent relevant experience may be considered.\nCertifications in Azure AI, AWS...","description_format":"text","description_chars":14564,"description_truncated":true,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity","Flexible schedule","Hybrid work","Professional 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