{"id":3791313,"url":"https://alion.io/job/hcltech-platform-engineer-ii-12","title":"Platform Engineer II","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"middle","employment_type":null,"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":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tokenization","optional":false},{"name":"CrewAI","optional":true},{"name":"FastAPI","optional":true},{"name":"Flask","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Rest API","optional":true},{"name":"Spring Boot","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-11-05T06:45:07Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-07T07:34:56Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Summary\nAs an Agentic AI Engineer II, you will work as a hands-on developer in the Cloud Native COE, building GenAI and agentic AI components under the guidance of senior architects and technical leads. You will write, test and maintain code for LLM-based agents, tool integrations and RAG pipelines, turn architect-defined designs into working proofs of concept and solution accelerators, and help bring them to production-ready quality.\nThis role suits engineers with 2-3 years of hands-on coding experience in Python and/or Java who want to deepen their skills in GenAI, agentic AI and cloud-native engineering by learning on the job.\nKey Responsibilities\nDevelop, test and debug agentic AI and GenAI components, such as LLM agents, tool/function calling and RAG pipelines, based on designs provided by architects\nDesign and optimize prompts for different LLMs, and evaluate model outputs to improve accuracy and reliability\nIntegrate LLM APIs (e.g., OpenAI, Azure OpenAI, AWS Bedrock) and vector databases with enterprise data sources\nBuild and maintain POCs, demos and reusable code components for customer use cases, including legacy modernization use cases\nImplement guardrails, logging and monitoring for AI services as directed by the technical lead\nDeploy and run services on cloud platforms (AWS, Azure or Google Cloud) using the team’s CI/CD pipelines and DevOps tools\nPrepare technical documentation, code walkthroughs and demo notes for architects and delivery teams\nTake part in code reviews, sprint ceremonies and internal knowledge-sharing sessions\nSkill Requirements\nProgramming & frameworks: Hands-on Python and/or Java; working experience with TensorFlow or PyTorch\nGenAI: Proficiency in GenAI frameworks and libraries such as OpenAI; prompt engineering - designing and optimizing prompts for various AI models to achieve the desired outputs and improve model performance\nAI/ML: Proficiency in core ML concepts and algorithms; knowledge of deep learning techniques and neural networks; understanding of NLP techniques and tools, including tokenization, embeddings,\ntransformers and language models; knowledge of vector databases and LLMs, and of integrating applications with them\nCloud computing: Experience working with cloud services on AWS, Azure or Google Cloud\n DevOps: Knowledge of DevOps practices and tools (e.g., Git, CI/CD pipelines, Docker)\nCommunication: Strong communication and articulation skills\nProblem solving: Ability to identify issues and come up with effective solutions\nLearnability & adaptability: Enthusiasm to learn new technologies; a quick learner who can learn on the job\nGood to Have:\nExposure to agentic AI frameworks such as LangChain, LangGraph, CrewAI or MCP\nExperience building REST APIs (e.g., FastAPI, Flask or Spring Boot)\nWorking knowledge of SQL and NoSQL databases\nOther Requirements\nEducation:\nBachelor’s or master’s degree in Engineering, Computer Science, or a related field\nGenAI or cloud certifications (AWS, Azure, GCP) are an advantage but not mandatory\nTeam & Client Interaction:\nSupport architects in customer workshops, demos and POC presentations by preparing and running technical demos\nExplain your work clearly to team members and, when required, to client technical teams\nWork closely with architects, senior engineers, and data and platform teams to deliver assigned tasks on time and to agreed quality standards\nLearning & Growth:\nStay current with new GenAI, agentic AI and cloud-native tools and frameworks, and share what you learn with the team\nPick up new technologies quickly and apply them on the job\nExperience:\n2-3 years of hands-on coding experience in Python and/or Java\nWorking experience with ML/DL frameworks such as TensorFlow or PyTorch\nHands-on experience using GenAI frameworks and libraries such as OpenAI, and in designing and optimizing prompts for LLMs\nExperience working with cloud services on AWS, Azure or Google 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