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.
Responsibilities:
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
- Own 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.
