AIML Developer

HCLTech; Dadri; On-site; INR 800,000 - 1,200,000; Full time

Job summary

HCLTech in Uttar Pradesh seeks candidates for GenAI development projects. You will develop applications using Python, AI frameworks, and integrate with enterprise data sources. We seek individuals with 1-4 years of relevant experience in this innovative field.

Mandatory qualifications include a B.E. or B.Tech in Computer Science or related disciplines, with a strong capability in Generative AI and Python programming. Join us in shaping the future of AI technology.

Qualifications

• 1-4 years of experience in GenAI development or related field.
• Hands-on experience building or deploying AI solutions.
• Strong programming capabilities in Python.

Responsibilities

• Develop GenAI applications using Python and AI frameworks.
• Build AI agents capable of reasoning and planning.
• Integrate GenAI with enterprise data sources.

Skills

Python programming

Generative AI knowledge

RAG architecture knowledge

Agentic AI concepts

Frameworks (LangChain, etc.)

LLMs integration

Education

B.E. / B.Tech in relevant engineering

MT / MSc in relevant disciplines

Job description

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

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

Preferred / Additional Skills

• Exposure to cloud‑native AI services, especially AWS Bedrock, Amazon SageMaker, Azure OpenAI, Azure AI Search, Google Vertex AI, or Gemini
• 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

Experience Criteria

• 1 to 4 years of relevant experience in GenAI development, AI application engineering, Python development, ML/NLP application development, backend development, or automation engineering
• Candidates with 0–1 year of experience should demonstrate capability through academic projects, internships, certifications, GitHub repositories, hackathons, prototypes, or hands‑on GenAI experiments
• Candidates 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

Mandatory Qualifications

• B.E. / B.Tech in Computer Science, Information Technology, Artificial Intelligence, Data Science, Electronics, Software Engineering, or any other relevant engineering discipline
• MT / MSc in Computer Science, Information Technology, Artificial Intelligence, Data Science, Machine Learning, Software Engineering, or related disciplines from a recognized institution or university