Lead Administrator (Support &Operations;)

Gautam Buddha Nagar, Uttar Pradesh

Job Summary

Applied AI Technologist

(AI Experience Technologist)

About the role

We're looking for an Applied AI Technologist who can turn a customer's business problem into a working, production-ready AI solution. You'll work at the point where customer conversations and hands-on building meet. You'll understand what the customer needs, picture the end state, build it quickly with AI-assisted development, and make sure it holds up in a real enterprise environment.

This is a builder role for someone with sound engineering judgment. Vibe coding lets you move fast, and your fundamentals decide what is good enough to ship.

Experience : 3 to 10 years

Key Responsibilities

Customer and requirements

- Work directly with customers to understand their business problems, clarify vague requirements, and turn them into clear solution outcomes.
- Picture the end state for customers and end users, and show it early with prototypes, mockups and demos.
- Get up to speed quickly on new domains and customer contexts, and know when to bring in specialists.
- Explain solutions, trade-offs and risks clearly to both business and technical stakeholders.

Building with AI

- Design and build solutions quickly with AI-assisted development tools (e.g., Claude Code, Cursor, GitHub Copilot).
- Guide LLMs precisely, with structured prompting, context engineering and step-by-step iteration, so the output is accurate and consistent.
- Build LLM-powered applications, including agentic workflows, RAG pipelines and tool or function-calling integrations where relevant.
- Design user experiences that are intuitive,



useful and fit how end users actually work.

Engineering quality and production readiness

- Review, test, debug and harden AI-generated code. Understand what the AI produced before shipping it, and reject what doesn't meet the bar.
- Take solutions from prototype to production: deployment, integration with customer systems (APIs, identity, data sources) and maintainable handover.
- Design evaluations, test cases and guardrails to measure and improve LLM output quality, reliability and safety.
- Apply security, data-privacy and responsible-AI practices in every solution, especially when handling customer data.

Skill Requirements

Required skills and experience

- Programming: Strong hands-on experience in Python and/or front-end (React, JavaScript/TypeScript) or back-end development, with solid software engineering fundamentals: code structure, testing, debugging, version control and basic architecture.
- AI-assisted development: Expert in vibe-coding workflows. Can break problems into steps, steer AI agents effectively, and review output critically.
- Prompting and LLM steering: Deep expertise in prompt design, including system prompts, few-shot examples, reasoning techniques, structured outputs, tool use and context management.
- LLM evaluation:



Experience defining success criteria, building test sets and evaluating model output systematically.
- Deployment and integration: Working knowledge of at least one cloud platform (Azure, AWS or GCP), APIs, containers or serverless, and CI/CD basics.
- Security and responsible AI: Understanding of data privacy, PII handling, access control, prompt-injection risks and enterprise compliance.
- Communication: Excellent written and spoken English. Can explain complex ideas clearly and concisely, and present with confidence to customers.
- Mindset: Learns rapid, takes ownership of outcomes, and knows what AI should and shouldn't be used to build.

Nice to have

- Experience with agentic frameworks (e.g., LangGraph, CrewAI, Semantic Kernel, MCP) or enterprise AI platforms (Azure AI Foundry, Copilot Studio, AWS Bedrock, Vertex AI).
- Experience in a customer-facing, consulting or pre-sales role.
- A background in UX, design thinking or product management.
- A portfolio of AI solutions, demos or open-source work that you've built and deployed.

Other Requirements

Selection process

- Screening conversation: background, communication and customer orientation.

- Live build exercise (90–120 minutes): Candidates get a deliberately vague customer brief. They are expected to:

- - ask questions to clarify the requirement
- build a working solution using AI tools of their choice
- walk through what they built and what they would fix, secure or test before production.

- Customer-scenario discussion: role-play presenting the solution to a business stakeholder.

📌 Lead Administrator (Support &Operations) (Gautam Buddh Nagar)
🏢 HCLTech
📍 Gautam Buddh Nagar