Head of Generative AI & MLOps (AI Center of Excellence)

About the Client:

that serves

The Mission:

AI transformation

Core Responsibilities & Impact Areas

1. AI Architecture & Scalability Leadership:

• Establish and Own the Generative AI Stack

  : Design and implement a robust, enterprise-level Generative AI architecture. This includes selecting and fine-tuning Large Language Models (LLMs), developing Retrieval-Augmented Generation (RAG) systems, and integrating domain-specific knowledge bases to build contextually aware AI solutions.

• Drive MLOps Excellence:

  Architect and govern scalable, secure, and resilient MLOps pipelines. Guarantee seamless, high-performance integration of AI models (including inference optimization) into our core enterprise systems.

• Data Foundation & Readiness

  : Define and execute the enterprise data architecture strategy required for AI. Ensure the availability, cleanliness, and completeness of data necessary for developing and training high-impact AI/ML models.

2. Strategic Delivery & Business Automation

• Accelerate AI-Driven Automation

  : Spearhead the development and deployment of AI-powered automation strategies that significantly reduce operational overhead, enhance efficiency, and unlock new business insights.

• Operationalize AI for Decision-Making:

  Embed sophisticated AI and business intelligence tools across the organization to automate workflows and enhance human decision-making and forecasting capabilities.

• Ensure Ethical and Compliant AI:

  Champion and enforce AI Ethics, governance, and regulatory compliance across all developed solutions to maintain trust and mitigate risk.

3. Team & Cross-Functional Leadership

• Build and Mentor a High-Performance Team

  : Lead, recruit, and mentor a diverse team of Data Scientists, MLOps Engineers, and AI Engineers. Foster a culture of technical rigor, continuous learning, and innovation.

• Executive Influence and Collaboration:

  Serve as the primary AI subject matter expert, presenting our vision, strategy, and results to the CTO and Senior Leadership. Partner closely with Product, Engineering, and Business teams to identify, prioritize, and drive the adoption of AI solutions across the organization.

Desired Experience & Technical Expertise

• Education: Master's degree preferred (or equivalent experience) in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.

• Leadership Track Record: Proven experience leading and scaling AI/ML teams and deploying solutions that delivered tangible, measurable business impact in an enterprise environment.

• Generative AI & LLMs: Deep, hands-on expertise in Generative AI, developing and fine-tuning Large Language Models (LLMs), and implementing advanced techniques like RAG.

• ML Frameworks: Expert-level proficiency with core AI/ML frameworks (e.g., PyTorch, TensorFlow) and optimization tools like ONNX and DeepSpeed.

• Deployment Experience: Extensive, practical experience in LLMOps, MLOps, Computer Vision, and/or NLP-based solutions from research through to scalable, production deployment.