Lead AI/ML Engineer & AI Strategy Lead

Role Overview

We are seeking a Lead AI/ML Engineer and AI Strategy Leader to drive the design, deployment, and governance of next-generation autonomous AI systems at scale across R&D. This is a senior individual contributor and strategic leadership role combining deep technical expertise in machine learning, agentic architectures, and industrial AI with the ability to define and execute enterprise-wide AI roadmaps.

The successful candidate will architect production-ready agentic systems, lead cross-functional AI initiatives across both research and development, and serve as a trusted technical advisor to senior stakeholders. This role is ideal for someone who thrives at the intersection of rigorous data science and high-impact business transformation.

Key Responsibilities

Agentic AI Architecture & Development

• Design and deploy enterprise-grade Agentic AI systems using LLM tool-calling, multi-step planning, and autonomous reasoning across structured (SQL) and unstructured (PDF, documents) data sources.
• Architect dual-agent and multi-agent frameworks including Data Orchestration Agents and Procurement/Supply Chain Optimization Agents
• Develop modular, reusable GenAI platforms enabling cross-functional teams to extend and repurpose core AI capabilities.
• Implement knowledge graphs and advanced NLP pipelines to power intelligent enterprise search and decision support.
• Lead development of predictive analytics models for asset performance degradation, survivability analysis, and maintenance optimisation across large industrial portfolios.

AI Strategy & Stakeholder Leadership

• Define and own the enterprise AI strategy and multi-year roadmap for AI/ML capabilities across R&D functions.
• Lead AI adoption initiatives, overcoming organisational resistance through explainability techniques and structured change management.
• Represent the AI function in steering committees, vendor evaluations, and industry partnerships.

Data Engineering & MLOps

• Architect and manage scalable Data Orchestration Layers integrating heterogeneous data sources
• Implement robust MLOps pipelines on Databricks (SQL Warehouse, Endpoints, MlFlow), ensuring data governance, model versioning, and production reliability.
• Design ETL workflows, data quality frameworks, and model health monitoring strategies to sustain AI performance post-deployment.
• Govern stochastic LLM outputs through structured validation, guardrails, and endpoint management.

Team Leadership & Delivery

• Drive end-to-end delivery of AI solutions using Agile / SAFe methodologies (Jira, Confluence), from ideation and prototyping through to production.

Required Qualifications

Education & Experience

• 5+ years of applied AI/ML experience in industrial, life sciences, energy, or complex enterprise environments.
• Demonstrated track record of delivering production AI systems with measurable business impact (cost savings, risk reduction, process efficiency).

Technical Skills

• Expert proficiency in Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, XGBoost).
• Deep expertise in Generative AI: LLM integration (OpenAI API), prompt engineering, RAG architectures, agentic frameworks (n8n or equivalent).
• Strong command of Databricks ecosystem: SQL Warehouse, Endpoints, MLflow, Volumes, Spark SQL.
• Experience with cloud platforms (AWS S3, Azure) and enterprise data systems (Salesforce, SharePoint, IBM Maximo CMMS).
• Proficiency in reinforcement learning frameworks, mixture density networks, and physics-informed modelling.
• Familiarity with industrial data systems: PI ProcessBook / OPC historians, SCADA, CMMS platforms.
• AWS Certified Cloud Practitioner or higher
• SAFe Practitioner / Certified Scrum Product Owner (CSPO) or equivalent Agile certification.

Preferred Qualifications

• Experience in pharma R&D with exposure to GxP, FDA, or equivalent compliance frameworks.
• Background in embedded systems, industrial control, or HVAC/process engineering providing domain depth for industrial AI applications.
• Proficiency with explainability frameworks (SHAP, LIME) and responsible AI governance practices.