Senior Consultant, Artificial Intelligence

Overview

As a Senior Consultant, Artificial Intelligence, you will play a pivotal role in shaping and delivering enterprise-grade, data-centric AI solutions that help clients solve complex business problems and drive measurable outcomes. You are a senior, self-directed consultant who operates comfortably across architecture, hands-on delivery, and client leadership. In this role, you will serve as a trusted advisor to client leaders while also acting as a technical lead and builder—designing, implementing, and scaling AI systems that sit on modern data platforms.

Your work will span agentic AI, RAG architectures, analytics + ML pipelines, and AI-enabled applications, primarily within the Microsoft ecosystem. You bring a strong engineering mindset—particularly with Python, SQL, Microsoft Fabric, Azure AI Foundry, and vector-based retrieval systems—and are comfortable owning solutions end to end, from data ingestion through AI inference and user-facing experiences.

• Lead & Advise:
  Serve as a day-to-day client lead for AI initiatives, advising executives and senior leaders on AI capabilities, architectural tradeoffs, risks, and adoption strategies.
• Translate business objectives into pragmatic AI and data roadmaps, aligned to data maturity, operating model, and governance constraints.
• Guide clients through build vs. buy decisions, platform selection (Fabric vs. Databricks), and AI operating model design.
• Design, Build & Deliver:
  Lead the end-to-end design and delivery of AI solutions, including RAG and agentic AI architectures, ML-enabled analytics and inference pipelines, AI-powered applications and copilots, and owning solution architecture decisions across data ingestion, transformation, storage, retrieval, model orchestration, and inference.
• Build and review production-grade code, ensuring scalability, observability, and maintainability.
• Integrate AI solutions with enterprise data platforms, ERP/CRM systems, and operational workflows.
• Data & Platform Engineering:
  Design and implement ETL/ELT pipelines using SQL and Python to support AI and analytics workloads; work hands-on with Microsoft Fabric (Lakehouse, Notebooks, Pipelines, Semantic Models) and/or Databricks; model and optimize data structures for analytics, ML training, and vector-based retrieval; ensure data quality, lineage, and performance across AI-enabled systems.
• Prototype & Innovate:
  Rapidly design and deliver POCs and MVPs to validate business value and technical feasibility; apply agile and iterative delivery approaches to accelerate time to value; develop reusable accelerators, reference architectures, and internal frameworks for AI delivery.
• Collaborate & Mentor:
  Partner with strategists, data engineers, ML engineers, developers, and change practitioners to deliver cohesive solutions; mentor consultants and analysts on AI architecture, data engineering, and modern delivery patterns; contribute to internal standards, best practices, and communities of practice for AI and data engineering.
• Integrate, Govern & Scale:
  Ensure AI solutions align with security, compliance, and responsible AI principles, including data privacy and auditability; support production hardening, monitoring, and lifecycle management of AI systems; help clients design governance models for LLMs, agents, data access, and model evaluation.

• Required Experience: 5+ years of experience in consulting, data engineering, analytics, or AI solution delivery; demonstrated experience delivering production-ready AI, ML, or advanced analytics solutions; proven ownership of client-facing work streams and technical delivery.
• Required Technical
  
  Skills:
  
  Backend development proficiency, with Python strongly preferred; strong experience with SQL-based data transformation, modeling, and analytics; hands-on experience with modern data platforms:
  Microsoft Fabric (preferred) and/or Databricks; experience with Azure AI Foundry, including LLM orchestration and AI application development; experience with vector databases and retrieval systems, with Azure AI Search preferred; solid understanding of ML concepts, including feature engineering, model evaluation, and…