{"id":3183062,"url":"https://alion.io/job/arrow-senior-generative-ai-agentic-ai-engineer","title":"Senior Generative AI / Agentic AI Engineer","company":{"id":1757520,"name":"Arrow Electronics","domain":"arrow.com","url":"https://alion.io/company/arrow-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"C","score":58,"open_postings":116,"ghost_share":0.698,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-25T05:49:26Z"}},"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":["Ahmedabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18000,"max_usd":37000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":38},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Agile","optional":true},{"name":"Amazon Neptune","optional":true},{"name":"AWS","optional":true},{"name":"Docker","optional":true},{"name":"Embeddings","optional":true},{"name":"FastAPI","optional":true},{"name":"Git","optional":true},{"name":"Hugging Face","optional":true},{"name":"Hybrid Search","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"Llama","optional":true},{"name":"Neo4j","optional":true},{"name":"Scrum","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Semantic Search","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-17T00:00:00Z","employer_posted_date":"2026-10-17","last_verified_at":"2026-10-24T05:29:10Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":8},"description":"Position:\nSenior Generative AI / Agentic AI EngineerJob Description:\nGen AI lead [primary ] and understanding of Data Engineering [secondary]\nRole summary:\nTeam lead focused on designing, building, and deploying production-grade generative AI and agentic-AI solutions.\nResponsible for delivering secure, scalable, and business-oriented AI systems that operate on structured and unstructured data and enable AI-driven decision-making\nBasic understanding of data engineering aspects like batch and streaming ingestion, transform data using Databricks (PySpark/SQL)\nKey Responsibilities\nLead the design, development, and deployment of scalable applications using Python or .NET.\nArchitect and implement Generative AI solutions, including RAG systems, LLM pipelines, prompt engineering, and model fine-tuning.\nWork with Knowledge Graph technologies to model, organize, and connect enterprise data.\nDesign cloud-native applications using Azure services such as Azure Functions, App Services, Azure AI Search, Cosmos DB, AKS, Azure OpenAI, Service Bus, Event Hub, and Storage.\nOversee the end-to-end technical delivery of projects, ensuring adherence to best practices, security, and performance standards.\nCollaborate with product owners, architects, and cross-functional teams to define technical roadmaps.\nManage and mentor engineering teams (5-15 members), including task planning, code reviews, and skill development.\nEnsure high-quality coding standards and maintain CI/CD pipelines.\nTroubleshoot and optimize distributed systems and cloud deployments.\nCommunicate project updates, risks, and technical decisions to stakeholders.\nRequired Skills & Qualifications\n10-12+ years of overall industry experience with at least 2-4 years in a Gen AI technical lead or team lead role.\nStrong development experience in Python or .NET (one primary, familiarity with the other is a plus).\nSolid hands-on experience with Generative AI and LLM-based systems (OpenAI, Azure OpenAI, Hugging Face, Llama, Phi, LangChain, Semantic Kernel, etc.).\nStrongPython and Azure engineering expertise with hands-on experience in FastAPI and Azure Functions, asynchronous and batch processing, and integration with Azure services including Blob Storage, Cosmos DB, Azure AI Search, Azure OpenAI, Event Hub, Logic Apps, and Durable Functions to build scalable, production-grade systems.\nProven ownership of end-to-end GenAI platforms, covering RAG pipelines, document intelligence, embedding generation and management, vector and graph database integrations, semantic search, and AI governance, with the ability to design, deploy, and operate enterprise-ready GenAI solutions on Azure.\nGood understanding of Knowledge Graphs, graph databases, ontologies, and graph modeling (Neo4j, AWS Neptune, Azure Cosmos DB Gremlin API, RDF, SPARQL, etc.).\nProven ability to design and deploy scalable, cloud-native solutions on Azure.\nStrong foundational knowledge of Azure cloud ecosystem (compute, network, data, identity, monitoring, DevOps).\nExperience managing small to medium-sized teams, handling project execution, planning, and delivery.\nHands-on experience with CI/CD pipelines, Git workflows, and DevOps practices.\nStrong analytical, problem-solving, and decision-making abilities.\nExcellent communication skills with the ability to articulate technical concepts to non-technical stakeholders.\nExperience with vector databases, RAG pipelines, embeddings, and enterprise search.\nKnowledge of MLOps and deploying ML/AI models in production.\nExposure to microservices architecture and container orchestration (Docker, Kubernetes/AKS).\nUnderstanding of security best practices in cloud and AI deployments.\nPrior experience working in agile/scrum environments.\nExperience in Database design : Cosmos DB (SQL API, indexing, partitioning)\nAzure & AI\n Azure OpenAI (Chat Completion, Embeddings, RAG pipelines)\nDocument Intelligence / Form Recognizer\n Azure AI Search (indexes, vector search, hybrid search)\nAzure Cognitive Services (Language, Vision, Speech)\n Azure Functions (durable and serverless workflows)\nAzure API Management\nFurther categorization of above JD [details to be referred in above sections]:\nMust have Skills:\nGenerative AI [vector databases, RAG pipelines, embeddings, and enterprise search.]\nPython / .Net\nLLM [Open AI [mandatory], model fine-tuning]\nDatabase Design: Cosmos DB (SQL API, indexing, partitioning)\nCloud [Azure]: AI Search (indexes, vector search, hybrid search), Azure Functions (durable and serverless workflows)\nUnderstanding of Knowledge Graphs, graph databases, ontologies, and graph modelling\nUpskilling in 2 to 4 weeks if timeframe:\nCI/CD pipelines, Git workflows, and DevOps practices\nExposure to microservices, Container orchestration (Docker, Kubernetes/AKS)\nAgentic AI Workflows\nSecurity best practices in cloud and AI deployments.\nGood to Have skills:\nMLOps and deploying ML/AI models in production\nDevelop batch and streaming ingestion, transform data using Databricks (PySpark/SQL)\nLocation:\nIN-GJ-Ahmedabad, India-Ognaj (eInfochips)Time Type:\nFull 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