{"id":2167572,"url":"https://alion.io/job/hcltech-track-manager-apache-kafka-windows-powershell-2","title":"Track Manager - Apache Kafka, Windows PowerShell","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"OpenAI","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-10-09T10:43:46Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-12T01:35:18Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary\nWe are seeking an experienced and hands-on Lead Engineer - Artificial Intelligence (AI) to design, develop, integrate, and operationalize AI solutions across the organization. The role will focus on translating business and operational requirements into production-ready AI applications, with a strong emphasis on Python engineering, Generative AI, Large Language Models (LLMs), Agentic AI, Machine Learning, Retrieval Augmented Generation (RAG), APIs, and intelligent automation. The successful candidate will work closely with engineering, operations, architecture, security, service management, tooling, data, and business teams to develop scalable and secure AI solutions that improve operational efficiency, automate repetitive activities, enhance decision-making, and improve service delivery. This is a hands-on technical role requiring strong software engineering fundamentals, advanced Python programming skills, practical experience building AI applications, and the ability to technically guide engineers through design and implementation. AI Engineering & Development\nDesign, develop, test, and maintain AI-powered applications and services using Python.\nBuild solutions leveraging Generative AI, LLMs, Agentic AI, Machine Learning, NLP, RAG, and intelligent automation.\nDevelop reusable Python modules, APIs, services, connectors, and AI components.\nBuild AI agents capable of interacting with enterprise applications, APIs, databases, knowledge repositories, and automation platforms.\nDevelop orchestration workflows integrating AI models with existing enterprise systems and operational processes.\nApply software engineering best practices including modular design, version control, automated testing, documentation, logging, and error handling.\nPerform code reviews and contribute to engineering standards and reusable development patterns. Solution Design & Implementation\nTranslate functional and business requirements into technical designs and implementation approaches.\nDevelop Proof of Concepts (PoCs), prototypes and Minimum Viable Products (MVPs) to validate AI use cases.\nContribute to solution architecture covering application components, APIs, AI models, data sources, vector stores, integrations, authentication, and deployment.\nCollaborate with enterprise architecture, security, infrastructure, cloud, data, and operations teams.\nSupport the transition of successful prototypes into scalable production solutions.\nTroubleshoot complex application, integration, model, and performance issues. Python & Software Engineering\nDevelop high-quality and maintainable Python applications for AI, automation and integration use cases.\nBuild REST APIs and backend services using appropriate Python frameworks.\nIntegrate applications with enterprise platforms through REST APIs, SDKs, databases, messaging systems and other interfaces.\nApply object-oriented programming, asynchronous programming, concurrency and appropriate software design patterns.\nImplement unit testing, integration testing and automated validation.\nUse Git-based development practices including branching, pull requests, code reviews and CI/CD.\nOptimize Python applications for reliability, scalability and performance. AI Operations & Production Readiness\nSupport deployment, monitoring, troubleshooting and lifecycle management of AI solutions.\nImplement appropriate application and model observability including logging, tracing, metrics and error handling.\nDevelop mechanisms to monitor AI solution quality, latency, usage, reliability and cost.\nSupport model and prompt versioning, testing and controlled releases.\nWork with platform and DevOps teams to automate build, deployment and configuration processes.\nEnsure AI solutions are supportable and maintainable within enterprise production environments. Responsible AI, Security & Governance\nImplement AI solutions in accordance with organizational security, privacy, compliance and Responsible AI\nKey Responsibilities\nWe are seeking an experienced and hands-on Lead Engineer - Artificial Intelligence (AI) to design, develop, integrate, and operationalize AI solutions across the organization. The role will focus on translating business and operational requirements into production-ready AI applications, with a strong emphasis on Python engineering, Generative AI, Large Language Models (LLMs), Agentic AI, Machine Learning, Retrieval Augmented Generation (RAG), APIs, and intelligent automation. The successful candidate will work closely with engineering, operations, architecture, security, service management, tooling, data, and business teams to develop scalable and secure AI solutions that improve operational efficiency, automate repetitive activities, enhance decision-making, and improve service delivery. This is a hands-on technical role requiring strong software engineering fundamentals, advanced Python programming skills, practical experience building AI applications, and the ability to technically guide engineers through design and implementation. AI Engineering & Development\nDesign, develop, test, and maintain AI-powered applications and services using Python.\nBuild solutions leveraging Generative AI, LLMs, Agentic AI, Machine Learning, NLP, RAG, and intelligent automation.\nDevelop reusable Python modules, APIs, services, connectors, and AI components.\nBuild AI agents capable of interacting with enterprise applications, APIs, databases, knowledge repositories, and automation platforms.\nDevelop orchestration workflows integrating AI models with existing enterprise systems and operational processes.\nApply software engineering best practices including modular design, version control, automated testing, documentation, logging, and error handling.\nPerform code reviews and contribute to engineering standards and reusable development patterns. Solution Design & Implementation\nTranslate functional and business requirements into technical designs and implementation approaches.\nDevelop Proof of Concepts (PoCs), prototypes and Minimum Viable Products (MVPs) to validate AI use cases.\nContribute to solution architecture covering application components, APIs, AI models, data sources, vector stores, integrations, authentication, and deployment.\nCollaborate with enterprise architecture, security, infrastructure, cloud, data, and operations teams.\nSupport the transition of successful prototypes into scalable production solutions.\nTroubleshoot complex application, integration, model, and performance issues. Python & Software Engineering\nDevelop high-quality and maintainable Python applications for AI, automation and integration use cases.\nBuild REST APIs and backend services using appropriate Python frameworks.\nIntegrate applications with enterprise platforms through REST APIs, SDKs, databases, messaging systems and other interfaces.\nApply object-oriented programming, asynchronous programming, concurrency and appropriate software design patterns.\nImplement unit testing, integration testing and automated validation.\nUse Git-based development practices including branching, pull requests, code reviews and CI/CD.\nOptimize Python applications for reliability, scalability and performance. AI Operations & Production Readiness\nSupport deployment, monitoring, troubleshooting and lifecycle management of AI solutions.\nImplement appropriate application and model observability including logging, tracing, metrics and error handling.\nDevelop mechanisms to monitor AI solution quality, latency, usage, reliability and cost.\nSupport model and prompt versioning, testing and controlled releases.\nWork with platform and DevOps teams to automate build, deployment and configuration processes.\nEnsure AI solutions are supportable and maintainable within enterprise production environments. Responsible AI, Security & Governance\nImplement AI solutions in accordance with organizational security, privacy, compliance and Responsible AI\nSkill Requirements\nMandatory Technical Skills Python Strong hands-on experience with Python is essential, including:\nObject-oriented programming\nData structures and algorithms\nREST API development and integration\nAsynchronous programming\nError handling and logging\nUnit and integration testing\nPackage and dependency management\nCode optimization\nSoftware design patterns\nGit-based development Experience with commonly used Python libraries and frameworks such as:\nFastAPI / Flask\nPydantic\nPandas / NumPy\nPyTest\nAI/ML and LLM SDKs/frameworks as appropriate Generative AI & LLMs Hands-on knowledge of:\nLarge Language Models\nPrompt engineering\nStructured outputs and tool/function calling\nRetrieval Augmented Generation (RAG)\nEmbeddings\nVector databases\nSemantic search\nContext management\nModel evaluation\nLLM integration patterns Experience with platforms/models such as:\nAzure OpenAI / OpenAI\nAnthropic\nGoogle Gemini\nOpen-source LLMs Agentic AI Practical understanding of:\nAI agents\nTool/API-enabled agents\nAgent orchestration\nMulti-step AI workflows\nAgent memory and context\nHuman-in-the-loop workflows\nGuardrails and controlled execution Experience with relevant agentic frameworks or SDKs is advantageous.\nOther Requirements\nMandatory Technical Skills Python Strong hands-on experience with Python is essential, including:\nObject-oriented programming\nData structures and algorithms\nREST API development and integration\nAsynchronous programming\nError handling and logging\nUnit and integration testing\nPackage and dependency management\nCode optimization\nSoftware design patterns\nGit-based development Experience with commonly used Python libraries and frameworks such as:\nFastAPI / Flask\nPydantic\nPandas / NumPy\nPyTest\nAI/ML and LLM SDKs/frameworks as appropriate Generative AI & LLMs Hands-on knowledge of:\nLarge Language Models\nPrompt engineering\nStructured outputs and tool/function calling\nRetrieval Augmented Generation (RAG)\nEmbeddings\nVector databases\nSemantic search\nContext management\nModel evaluation\nLLM integration patterns Experience with platforms/models such as:\nAzure OpenAI / OpenAI\nAnthropic\nGoogle Gemini\nOpen-source LLMs Agentic AI Practical understanding of:\nAI agents\nTool/API-enabled agents\nAgent orchestration\nMulti-step AI workflows\nAgent memory and context\nHuman-in-the-loop workflows\nGuardrails and controlled execution Experience with relevant agentic frameworks or SDKs is 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