Walk-in Drive for Gen AI Developer - Noida Sec 60 on 20th June

Walk-in Drive

Role: Gen AI Developer

Experience Range: 1-4 Years

Date: 20-Jun-26 (Saturday)

Time: 10:30 AM

Place: HCL Tech Office, Noida Sec - 60 (A8/9, Block A, HCLTech , Sector 60, Noida, Uttar Pradesh)

Please fill this online form without fail: HCL Tech | Gen AI Developer | Walk In Drive | Candidate Information Form – Fill out form

NOTE: Please bring a hardcopy of your updated resume and don't bring any electronic devices other than your mobile phone.

About the Role

We 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.

This role is suitable for candidates with 1 to 4 years of full-time 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.

Key Responsibilities

• Develop GenAI applications using Python, LLM APIs, prompt engineering, RAG patterns, embeddings, vector search, and agentic AI frameworks. Build AI agents capable of reasoning, planning, tool calling, function calling, workflow orchestration, memory usage, task decomposition, and multi-step execution.
• Design 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.
• Integrate GenAI solutions with structured and unstructured enterprise data sources such as documents, databases, SharePoint repositories, knowledge bases, APIs, ticketing systems, and workflow platforms.
• Implement prompt templates, system prompts, reusable prompt libraries, structured outputs, JSON response formats, prompt versioning, and output validation logic.
• Create tool integrations that allow agents to call APIs, execute workflows, retrieve data, summarize content, classify information, generate reports, and trigger downstream actions safely.
• Support model selection and configuration based on use case needs such as accuracy, latency, context window, token usage, cost, privacy, and deployment constraints. Integrate LLMs with enterprise systems, APIs, databases, knowledge repositories, search services, and automation workflows.
• Use frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar tools to build agentic workflows.
• Create reusable components for prompt templates, tool integrations, retrieval workflows, memory handling, guardrails, model evaluation, tracing, observability, and monitoring.
• Perform testing and evaluation of GenAI outputs for factual accuracy, relevance, hallucination control, groundedness, safety, bias, latency, token usage, cost, and reliability.
• Implement basic Responsible AI and security controls such as input validation, prompt injection mitigation, PII handling, access control, auditability, and safe response handling.
• Support deployment and integration of GenAI components into backend services, web applications, automation workflows, and cloud-hosted environments.
• Support testing and evaluation of GenAI outputs for accuracy, relevance, hallucination control, latency, cost, safety, and reliability.
• Collaborate with data engineers, backend developers, cloud engineers, QA teams, product owners, and business stakeholders to deliver AI-enabled features.
• Document prompts, agent flows, API integrations, model configurations, assumptions, limitations, and deployment steps.

Mandatory Technical Skills

• Strong programming capability in Python, including data structures, APIs, object-oriented programming, exception handling, logging, debugging, package management, and modular application development.
• Hands-on exposure to Generative AI, Large Language Models, prompt engineering, embeddings, tokenization, context windows, structured outputs, and AI application development.
• Working knowledge of RAG architecture, including document processing, chunking strategies, metadata design, vectorization, semantic search, hybrid search, reranking, context augmentation, and grounded response generation.
• Experience 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.
• Exposure to frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent agentic development frameworks.
• Experience 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.
• Working knowledge of vector databases or semantic search platforms such as FAISS, Pinecone, Chroma, Weaviate, OpenSearch, Azure AI Search, or pgvector.
• Understanding of REST APIs, JSON, authentication, backend integration, data ingestion, and application deployment basics.
• Working knowledge of Git, code reviews, debugging practices, documentation, and Agile delivery methods.

Preferred / Additional Skills

• Exposure to cloud-native AI services, especially 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 flows, and agent evaluation methods.
• Knowledge of LLMOps or GenAIOps practices, including prompt versioning, model configuration management, monitoring, tracing, evaluation, and cost tracking.