AI Engineer Senior Consultant

Role Overview

What You’ll Do

• Build agentic AI use cases —
• design, develop, and deploy autonomous and semi-autonomous agents that plan, reason, use tools, and orchestrate multi-step workflows to solve real business problems.
• Develop on multiple platforms —
• implement solutions across Microsoft Copilot Studio (low-code/pro-code agents and copilots), Azure AI Foundry (model deployment, orchestration, and evaluation), and Claude via the Anthropic API and agent frameworks.
• Design the architecture —
• define solution architecture for AI systems, including agent orchestration, tool/function calling, retrieval-augmented generation (RAG), memory, guardrails, and integration with enterprise data and applications.
• Engineer the prompt and context layer —
• craft and optimize prompts, context strategies, and tool definitions; build evaluation harnesses to measure quality, safety, and reliability.
• Integrate with the enterprise —
• connect agents to APIs, databases, knowledge bases, and Microsoft 365 / Azure services using secure, well-governed patterns.
• Advise and lead —
• work directly with clients and internal teams to shape use cases, run proofs of concept, estimate effort, and guide junior engineers.
• Operationalize responsibly —
• apply best practices for testing, monitoring, cost control, observability, and responsible AI throughout the lifecycle.

Core Experience

• 5+ years in software/data/AI engineering, with at least 1–2 years building LLM-based or agentic AI applications.
• Proven, hands-on delivery of agentic or copilot solutions in production or advanced proof-of-concept settings.
• Strong programming skills in Python
• (comfort with JavaScript/TypeScript or C# is a plus).

Platform Expertise

• Microsoft Copilot Studio —
• building agents, topics, actions, connectors, and integrations within the Power Platform ecosystem.
• Azure AI Foundry —
• deploying and orchestrating models, building RAG pipelines, and using its evaluation and safety tooling.
• Anthropic Claude —
• developing with the Claude API, tool use / function calling, agent loops, and prompt design; familiarity with the Model Context Protocol (MCP) and agentic frameworks is a strong plus.

AI & ML Foundations

• Solid understanding of machine learning fundamentals —
• supervised vs. unsupervised learning, model training and evaluation, embeddings, and where classical ML fits alongside LLMs.
• Strong grasp of generative AI concepts —
• transformers and LLM behavior, RAG, fine-tuning vs. prompting trade-offs, evaluation, and hallucination mitigation.

AI Architecture

• Working knowledge of how AI systems are architected — agent orchestration patterns, vector stores and retrieval, API and event-driven integration, security and identity, scalability, and cost/performance trade-offs.
• Ability to produce clear architecture diagrams and design decisions that non-technical stakeholders can follow.

Consulting Skills

• Excellent communication and stakeholder-management skills; comfortable presenting to and advising senior client audiences.
• Ability to scope ambiguous problems, manage delivery, and mentor others.
• Nice to Have
• Experience with multi-agent frameworks (e.g., LangGraph, Semantic Kernel, AutoGen, CrewAI).
• Familiarity with cloud platforms (Azure preferred; AWS/GCP welcome) and DevOps/MLOps practices.
• Exposure to responsible AI, governance, and enterprise data-security frameworks.
• Relevant certifications (e.g., Azure AI Engineer Associate).