AI Architect

Hands-on experience applying Design Thinking and System Thinking methodologies in real-world scenarios.

Experience working in an agile environment with 100+ team members.

Experience leading in a Technology Leadership role for large-scale delivery programs.

Experience driving proactive, technology-led programs focused on proof of concepts, innovation, and the development of solution assets.

Experience analyzing, implementing, and evaluating IT systems and their specifications, with a strong understanding of the breadth and depth of technologies.

Experience across multiple technologies, including Java, J2EE, JavaScript, Spring, Hibernate, web services, .NET Framework, .NET Core, WEB API, Angular, ExtJS, Node JS, Python, Big Data, Cloud services, and more.

Experience designing and implementing Microservices, SOA, modern web applications, and data-driven solutions within a cloud-native ecosystem.

A clear understanding of AWS and Azure cloud services, including deployment models.

Experience with IaaS and PaaS platforms, as well as SaaS-based service offerings.

Strong technical aptitude with the ability to quickly learn new technologies.

Excellent analytical, interpersonal, and communication skills.

Excellent team player with strong problem-solving and troubleshooting capabilities.

Experience developing and implementing enterprise IT strategy for clients.

Expert-level knowledge and hands-on experience in App Development.

Experience in API design, API development, and API management.

Hands-on experience in DB design, database architecture, and optimization.

Good knowledge of Data Strategy, Big Data, and Machine Learning concepts.

Leadership

Excellent organizational and leadership skills.

Experience controlling information technology budgets.

Proven experience as an IT Director or in a similar leadership role.

Communication

Ability to communicate technology in an easy-to-understand manner.

Outstanding communication abilities.

Experience with client communication, including working with clients on requirements and delivery.

Other skills

Strategic thinking and problem-solving aptitude.

AI & Agentic Engineering Expertise (Mandatory)

Agentic Engineering Models

- Deep understanding of AI-led SDLC, including how AI agents contribute to planning, coding, review, testing, and deployment phases

- Experience designing multi-agent systems with defined roles (orchestrator, coder, reviewer, tester agents), task routing, and state management

- Familiarity with agent frameworks: Claude Agents SDK, or equivalent

GenAI Developer Tooling (Hands-On)

- Direct experience using and configuring:

- Claude Code (Anthropic) — CLI-driven agentic coding, MCP server setup, custom slash commands, hooks, and CLAUDE.md context engineering

- Ability to configure, extend, and govern these tools at enterprise scale

MCP & Agent Orchestration

- Experience with MCP (Model Context Protocol), including designing and deploying MCP servers to expose tools, APIs, and data sources to AI agents

- Understanding of tool use/function calling patterns for LLM-driven agents

- Familiarity with prompt chaining, RAG pipelines, and context window management strategies

Prompt & Context Engineering

- Proficiency in prompt engineering, including system prompts, few-shot examples, chain-of-thought, and structured output design

- Experience building context engineering frameworks, including CLAUDE.md / Copilot instructions files, knowledge fabric design, and retrieval-augmented generation for code-aware agents

- Understanding of model evaluation, including benchmarking GenAI output quality for coding and testing tasks

Tooling & Platform Strategy

- Claude Code (Anthropic) — agentic coding, file-level reasoning, and CLI-driven automation

Engineering Enablement

- Build and operationalize:

- Scaffolding frameworks for requirements drafting, code generation, and test automation

- AI accelerators for synthetic data generation, intelligent regression, and defect prediction

- Context engineering pipelines, including managing prompts, embeddings, knowledge graphs, and retrieval-augmented generation (RAG) for code-aware AI agents

- Configure and govern MCP (Model Context Protocol) servers and tool integrations to extend agent capabilities across enterprise systems

Governance & Standards

- Establish AI usage governance covering prompt standards, context security, model selection criteria, and output quality controls

- Define guardrails for responsible AI use in engineering, including IP protection, hallucination mitigation, and compliance alignment

- Build and maintain AI engineering playbooks, runbooks, and standards for adoption at scale