HCLTech Hiring Senior RPA Developer - Core in Noida | Onsite

Job Overview

HCLTech lists the position as Senior RPA Developer - Core in Noida, Uttar Pradesh. However, the supplied job summary and detailed responsibilities describe a GenAI Agentic Developer role. This distinction is retained here rather than silently changing the official displayed title.

The described work involves designing, developing, integrating, testing and supporting intelligent applications powered by large language models, Retrieval-Augmented Generation (RAG), tool-calling workflows, autonomous agents and agentic AI frameworks. These applications may connect LLMs with enterprise data, APIs, workflow systems, vector databases and cloud AI services.

Key Responsibilities

• Develop GenAI applications using Python, LLM APIs, prompt engineering, embeddings, vector search, RAG patterns and agentic AI frameworks.
• Build agents that support reasoning, planning, tool and function calling, memory, task decomposition, workflow orchestration and multi-step execution.
• Design RAG and Agentic RAG pipelines covering document ingestion, parsing, chunking, metadata tagging, embeddings, vector indexing, semantic and hybrid search, reranking, prompt construction and grounded response generation.
• Integrate solutions with enterprise documents, databases, SharePoint repositories, knowledge bases, APIs, ticketing systems and workflow platforms.
• Create prompt templates, system prompts, reusable prompt libraries, structured JSON outputs, prompt versioning and output validation logic.
• Build safe tool integrations that allow agents to call APIs, execute workflows, retrieve data, summarize and classify information, generate reports and trigger downstream actions.
• Select and configure models according to accuracy, latency, context-window needs, token usage, cost, privacy and deployment constraints.
• Develop reusable components for retrieval, memory handling, guardrails, model evaluation, tracing, observability and monitoring.
• Evaluate GenAI outputs for factual accuracy, relevance, groundedness, hallucination, safety, bias, latency, token consumption, cost and reliability.
• Apply responsible AI and security controls, including input validation, prompt-injection mitigation, PII handling, access control, auditability and safe response handling.
• Support deployment into backend services, web applications, automation workflows and cloud-hosted environments.
• Collaborate with data engineers, backend and cloud developers, QA teams, product owners and business stakeholders. Document prompts, agent flows, integrations, model configurations, assumptions and deployment steps.

Mandatory Technical Skills

• Python: Data structures, APIs, object-oriented programming, exception handling, logging, debugging, package management and modular application development.
• Generative AI and LLMs: Hands-on exposure to GenAI, large language models, prompt engineering, embeddings, tokenization, context windows, structured outputs and AI application development.
• RAG architecture: Document processing, chunking strategies, metadata design, vectorization, semantic and hybrid search, reranking, context augmentation and grounded response generation.
• Agentic AI: Tool and function calling, planning, memory, reflection, reasoning loops, task decomposition, autonomous execution, human-in-the-loop flows and workflow orchestration.
• Frameworks: Exposure to LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or similar agentic development frameworks.
• Model integration: APIs or cloud AI services such as AWS Bedrock, Azure OpenAI, Google Vertex AI, OpenAI APIs, Anthropic APIs or open-source model endpoints.
• Vector search: Knowledge of FAISS, Pinecone, Chroma, Weaviate, OpenSearch, Azure AI Search or pgvector.
• Backend integration: REST APIs, JSON, authentication, data ingestion and application deployment basics.
• Engineering practices: Git, code reviews, debugging, documentation and Agile delivery methods.

Preferred and Additional Skills

• Exposure to AWS Bedrock, Amazon SageMaker, Azure OpenAI, Azure AI Search, Google Vertex AI or Gemini APIs.
• Familiarity with multi-agent systems, supervisor-agent patterns, planner-executor workflows, human-in-the-loop processes and agent evaluation methods.
• Knowledge of LLMOps or GenAIOps, including prompt versioning, model configuration management, monitoring, tracing, evaluation and cost tracking.
• Exposure to OCR, document intelligence, NLP, text classification, summarization, entity extraction and enterprise knowledge search.
• Understanding of model evaluation, hallucination reduction, grounded responses, guardrails, responsible AI, privacy and security controls.
• Experience with FastAPI, Flask, Streamlit, React, Node.js or similar tools for AI-enabled applications and demos.

Experience Criteria

The stated experience range is 0 to 5 years in GenAI development, AI application engineering, Python development, ML/NLP application development, backend development or automation engineering.

• 0–1 year: Applicants should demonstrate relevant capability through academic projects, internships, certifications, GitHub repositories, hackathons, prototypes or hands-on GenAI experiments.
• 2–5 years: Applicants should have practical experience building, integrating, testing or deploying GenAI, AI assistant, chatbot, RAG, automation or agentic workflow solutions.

Educational Qualifications

The mandatory qualification is a B.E. or B.Tech in Computer Science, Information Technology, Artificial Intelligence, Data Science, Electronics, Software Engineering or another relevant engineering stream.

Equivalent qualifications may also be considered: BCA, MCA, M.Tech or M.Sc. in Computer Science, Information Technology, Artificial Intelligence, Data Science, Machine Learning, Software Engineering or related disciplines from a recognized institution or university.

Technologies and Tools

• Programming and application development: Python, FastAPI, Flask, React and Node.js.
• GenAI: Generative AI, LLMs, prompt engineering, embeddings, tokenization and structured outputs.
• Agent frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen and CrewAI.
• Model services: AWS Bedrock, Amazon SageMaker, Azure OpenAI, Google Vertex AI, OpenAI APIs, Anthropic APIs and Gemini APIs.
• Vector search: FAISS, Pinecone, Chroma, Weaviate, OpenSearch, Azure AI Search and pgvector.
• Integration: REST APIs, JSON and Git.

Work Location

The supplied description identifies Noida, Uttar Pradesh, India as the location. It does not explicitly state a remote or hybrid arrangement, so the configured work mode remains onsite.

Who Should Apply

Candidates with Python and GenAI application development skills may be suitable. Entry-level applicants should be ready to demonstrate practical learning through projects, internships, certifications, GitHub repositories, hackathons or working prototypes. Candidates with more experience should be able to explain their contribution to building, integrating, testing or deploying GenAI, RAG, chatbot, automation or agentic workflow solutions.

Role Title Clarification

The displayed title is Senior RPA Developer - Core, but the supplied summary and skills describe a GenAI Agentic Developer profile. Applicants should verify the role title and details on the employer's application page.

How to Apply

Review the role details on the HCLTech careers page and follow the employer's application process.

Apply now through SoftoJobs to explore this opportunity.