{"id":2167590,"url":"https://alion.io/job/hcltech-senior-rpa-developer-core-4","title":"Senior RPA Developer - Core","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":"Enterprise Apps","role_family":"Enterprise Apps","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15000,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":2,"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":"Amazon SageMaker","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"Node JS","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SharePoint","optional":false},{"name":"Streamlit","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tokenization","optional":false},{"name":"Tool Use","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false},{"name":"JavaScript","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-09T10:43:46Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-13T05:49:19Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Job Summary\nAbout the Role\nWe are looking for a GenAI Agentic Developer to design, build, integrate, test, and support intelligent applications powered by Large Language Models, Retrieval-Augmented Generation, tool-calling workflows, autonomous agents, and agentic AI frameworks. The role requires strong programming fundamentals, practical exposure to GenAI application development, and the ability to connect LLMs with enterprise data sources, APIs, workflow systems, vector databases, and cloud-based AI services. The candidate should be able to contribute to AI assistants, chatbots, knowledge search solutions, document intelligence use cases, automation agents, and production-ready GenAI features with appropriate controls for accuracy, reliability, safety, cost, and performance.\nThis role is suitable for candidates with 0 to 5 years of relevant experience. Freshers or entry-level candidates should demonstrate strong project, internship, certification, GitHub, hackathon, academic, or portfolio-based exposure in Python, GenAI, LLM applications, RAG pipelines, prompt engineering, vector search, chatbot development, AI-assisted automation, or agentic workflow prototypes.\nKey Responsibilities\nKey Responsibilities\nDevelop GenAI applications using Python, LLM APIs, prompt engineering, RAG patterns, embeddings, vector search, and agentic AI frameworks.\nBuild AI agents capable of reasoning, planning, tool calling, function calling, workflow orchestration, memory usage, task decomposition, and multi-step execution.\nDesign and implement RAG and Agentic RAG pipelines using document ingestion, parsing, chunking, metadata tagging, embeddings, vector indexing, semantic search, hybrid retrieval, reranking, prompt construction, and grounded response generation.\nIntegrate GenAI solutions with structured and unstructured enterprise data sources such as documents, databases, SharePoint repositories, knowledge bases, APIs, ticketing systems, and workflow platforms.\nImplement prompt templates, system prompts, reusable prompt libraries, structured outputs, JSON response formats, prompt versioning, and output validation logic.\nCreate tool integrations that allow agents to call APIs, execute workflows, retrieve data, summarize content, classify information, generate reports, and trigger downstream actions safely.\nSupport model selection and configuration based on use case needs such as accuracy, latency, context window, token usage, cost, privacy, and deployment constraints.\nIntegrate LLMs with enterprise systems, APIs, databases, knowledge repositories, search services, and automation workflows.\nUse frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar tools to build agentic workflows.\nCreate reusable components for prompt templates, tool integrations, retrieval workflows, memory handling, guardrails, model evaluation, tracing, observability, and monitoring.\nPerform testing and evaluation of GenAI outputs for factual accuracy, relevance, hallucination control, groundedness, safety, bias, latency, token usage, cost, and reliability.\nImplement basic Responsible AI and security controls such as input validation, prompt injection mitigation, PII handling, access control, auditability, and safe response handling.\nSupport deployment and integration of GenAI components into backend services, web applications, automation workflows, and cloud-hosted environments.\nSupport testing and evaluation of GenAI outputs for accuracy, relevance, hallucination control, latency, cost, safety, and reliability.\nCollaborate with data engineers, backend developers, cloud engineers, QA teams, product owners, and business stakeholders to deliver AI-enabled features.\nDocument prompts, agent flows, API integrations, model configurations, assumptions, limitations, and deployment steps.\nSkill Requirements\nMandatory Technical Skills\nStrong programming capability in Python, including data structures, APIs, object-oriented programming, exception handling, logging, debugging, package management, and modular application development.\nHands-on exposure to Generative AI, Large Language Models, prompt engineering, embeddings, tokenization, context windows, structured outputs, and AI application development.\nWorking knowledge of RAG architecture, including document processing, chunking strategies, metadata design, vectorization, semantic search, hybrid search, reranking, context augmentation, and grounded response generation.\nExperience or strong project exposure in agentic AI concepts such as tool calling, function calling, planning, memory, reflection, reasoning loops, task decomposition, autonomous execution, human-in-the-loop flows, and workflow orchestration.\nExposure to frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent agentic development frameworks.\nExperience integrating LLMs through APIs or cloud AI services such as AWS Bedrock, Azure OpenAI, Google Vertex AI, OpenAI APIs, Anthropic APIs, or open-source model endpoints.\nWorking knowledge of vector databases or semantic search platforms such as FAISS, Pinecone, Chroma, Weaviate, OpenSearch, Azure AI Search, or pgvector.\nUnderstanding of REST APIs, JSON, authentication, backend integration, data ingestion, and application deployment basics.\nWorking knowledge of Git, code reviews, debugging practices, documentation, and Agile delivery methods.\nPreferred / Additional Skills\nExposure to cloud-native AI services, especially AWS Bedrock, Amazon SageMaker, Azure OpenAI, Azure AI Search, Google Vertex AI, or Gemini APIs.\nFamiliarity with multi-agent systems, supervisor-agent patterns, planner-executor workflows, human-in-the-loop flows, and agent evaluation methods.\nKnowledge of LLMOps or GenAIOps practices, including prompt versioning, model configuration management, monitoring, tracing, evaluation, and cost tracking.\nExposure to OCR, document intelligence, NLP, text classification, summarization, entity extraction, and enterprise knowledge search use cases.\nBasic understanding of model evaluation metrics, hallucination reduction, grounded responses, guardrails, responsible AI, privacy, and security controls.\nExperience with FastAPI, Flask, Streamlit, React, Node.js, or similar tools for building AI-enabled applications and demos.\nOther Requirements\nExperience Criteria\n0 to 5 years of relevant experience in GenAI development, AI application engineering, Python development, ML/NLP application development, backend development, or automation engineering.\nCandidates with 0-1 year of experience should demonstrate capability through academic projects, internships, certifications, GitHub repositories, hackathons, prototypes, or hands-on GenAI experiments.\nCandidates with 2-5 years of experience should have hands-on experience building, integrating, testing, or deploying GenAI, AI assistant, chatbot, RAG, automation, or agentic workflow solutions.\nEducational Qualifications\nMandatory Qualification:\nB.E. / B.Tech in Computer Science, Information Technology, Artificial Intelligence, Data Science, Electronics, Software Engineering, or any other relevant engineering stream.\nEquivalent qualifications may also be considered:\nBCA / MCA / M.Tech / M.Sc. in Computer Science, Information Technology, Artificial Intelligence, Data Science, Machine Learning, Software Engineering, or related disciplines from a recognized institution or 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