Job Summary

Applied AI Technologist

(AI Experience Technologist)

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

• 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 fast, 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

1. Screening conversation: background, communication and customer orientation.
2. 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.

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