We are seeking a hands-on AI Engineer to design, build, integrate, and operationalize enterprise-grade AI solutions across the organization. The role will focus on practical AI engineering using Microsoft Copilot, Copilot Studio with the underlying models from OpenAI, Anthropic, and modern Generative AI capabilities to improve business processes, knowledge access, productivity, and digital service delivery. You will work closely with business stakeholders, IT delivery teams, data owners, and vendors to translate business needs into secure, scalable, and maintainable AI-enabled solutions. The ideal candidate combines strong technical AI development experience with sound business understanding, clear communication skills, and the ability to deliver AI solutions responsibly within an enterprise environment
- AI Solution Engineering & Development
- Design, develop, test, and deploy AI-enabled solutions using Copilot, Copilot Studio, LLM APIs, orchestration frameworks, and enterprise integration patterns.
- Build practical GenAI use cases such as conversational assistants, knowledge retrieval experiences, workflow copilots, document intelligence, summarization, and productivity automation.
- Develop prototypes and production-ready AI components that integrate with enterprise systems, data sources, APIs, and Microsoft 365 services.
- Copilot, LLM & Agentic AI Delivery
- Configure and extend Microsoft 365 Copilot, Copilot Studio agents, custom copilots, topics, actions, connectors, prompts, and enterprise knowledge grounding patterns.
- Work with OpenAI / ChatGPT and Claude models, including effective prompt design, model selection, response evaluation, guardrails, context handling, and performance optimization.
- Apply Retrieval-Augmented Generation (RAG), embeddings, vector search, function calling, tool use, and agentic workflow concepts where appropriate.
- Integration, Data & Technical Architecture
- Collaborate Techno-Functional developers to define AI solution designs, integration approaches, APIs, data flows, authentication, and security boundaries.
- Connect AI solutions to structured and unstructured enterprise data sources while considering data quality, permissions, sensitivity, and governance requirements.
- Produce technical documentation including architecture notes, integration specifications, prompt libraries, model evaluation notes, and operational runbooks.
- Responsible AI, Security & Governance
- Embed responsible AI principles, privacy, compliance, security, and risk considerations into AI solution design and delivery from the start.
- Support model and prompt evaluation, hallucination control, access control validation, data leakage prevention, and safe-use guidance for business users.
- Contribute to AI governance artefacts, standards, reusable patterns, and technical controls for enterprise AI adoption.
- Business Engagement & Use Case Delivery
- Work with business stakeholders to understand pain points, assess AI feasibility, refine requirements, and translate business needs into technical AI solutions.
- Support discovery workshops, solution demos, user testing, adoption activities, and continuous improvement cycles for AI-enabled products and services.
- Balance technical feasibility, business value, user experience, delivery effort, and operational sustainability when shaping AI use cases.
- Quality, DevOps & Continuous Improvement
- Apply software engineering practices including version control, code review, testing, environment management, deployment discipline, and ongoing monitoring.
- Evaluate AI outputs using defined quality criteria, feedback loops, test datasets, prompt regression checks, and business acceptance measures.
- Stay current with developments across Copilot, OpenAI, Claude, Azure AI, GenAI frameworks, and emerging enterprise AI patterns.