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Location
In office (Chennai)
Seniority
Staff · 5+ years exp
Visa
Sponsors visas in Ireland; no register of this country names it

Confirmed on the employer's own hiring board on Nov 12, 2026. First seen by Alion on Nov 12, 2026.

Overview
Company
Impact
Profile match
HCLTech is an Indian information technology services and consulting company headquartered in Noida, Uttar Pradesh. Its roots lie in HCL, founded in 1976 by Shiv Nadar and partners, and the IT services business was set up as a separate company in 1991. It provides application development and maintenance, infrastructure and cloud services, cybersecurity, engineering and R&D services, and sells its own software products through HCLSoftware. The company employs more than 200,000 people in some 60 countries and is listed on the Indian stock exchanges.

Job Summary

We are looking for a highly skilled Full Stack Engineer to join our team and build the core portal for our Agentic AI platform. You will be responsible for designing, developing, and scaling end-to-end applications that leverage generative AI models and multi-agent architectures. This role bridges the gap between sophisticated AI models and user-facing applications, requiring expertise across the frontend, backend, and the critical middleware that orchestrates it all.

If you are passionate about crafting elegant user experiences and converting cutting-edge AI capabilities into production-ready solutions, this role is for you.

Key Responsibilities

  • Frontend Development: Build responsive, modern web interfaces and dashboards using React, Next, Express, TypeScript, and modern CSS frameworks (like Tailwind). Ensure intuitive user experiences for interacting with and managing AI agents.
  • Backend Engineering: Develop scalable backend services and APIs using Python, Node.js, or Go. Manage databases (SQL, NoSQL, Vector) to support AI features and optimize data models.
  • Middleware & API Integration: Design and implement robust middleware to orchestrate complex multi-agent workflows. Build API gateways handling routing, validation, authentication, and rate limiting. Connect AI agents securely with third-party external tools and business systems via REST/GraphQL APIs.
  • Agentic AI Implementation: Implement and maintain AI agent logic utilizing frameworks such as LangChain, LangGraph, AutoGen, or CrewAI. Architect memory systems (short/long-term), implement tool-calling capabilities, and design ReAct loops for reasoning and action.
  • DevOps & Observability: Implement CI/CD pipelines (DevOps/MLOps) to deploy and monitor services. Establish comprehensive observability (logging, tracing) to track agent performance, understand behavior in production, and improve reliability.

Collaboration: Work closely with AI engineers, data scientists, product managers, and UI/UX designers to deliver cohesive, end-to-end features

Skill Requirements

  • 5+ years of software engineering experience building scalable, full-stack products.
  • Frontend Skills: Proficiency in modern JavaScript (ES6+)/TypeScript, React.js, and state management.
  • Backend & Middleware Skills: Strong experience in Python, Node.js, or similar languages. Deep understanding of API development (REST/GraphQL), distributed systems concepts (concurrency, scaling), and API gateway patterns.
  • Database Knowledge: Experience with PostgreSQL, MongoDB, or similar, plus familiarity with Vector Databases (e.g., Pinecone, FAISS).
  • AI/ML Exposure: Proven experience building or deploying LLM-based applications (RAG, prompt engineering, tool integration). Hands-on experience with Agentic AI frameworks (LangChain, LangGraph, etc.).
  • Infrastructure: Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

Problem Solving: Critical thinker with strong algorithmic skills and the ability to debug complex systems across language boundaries

Other Requirements

  • Experience with Model Context Protocol (MCP) design or implementation.
  • Background in building evaluation/benchmarking systems for agent workflows.
  • Familiarity with building knowledge graphs and evaluating LLM outputs.
  • Experience fine-tuning models or deploying local open-source models.
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