At a Glance
- Tasks: Lead AI strategy and execution across business domains, driving innovation and transformation.
- Company: Join a leading tech firm focused on AI and intelligent automation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic role with global impact and career advancement opportunities.
- Why this job: Shape the future of AI while collaborating with top industry leaders.
- Qualifications: Expertise in Python, .NET, and cloud technologies required.
The predicted salary is between 70100 - 100000 £ per year.
The Director for AI & Intelligent Automation will define and execute the enterprise strategy for Artificial Intelligence, Machine Learning, and Automation across business domains. This role blends technical excellence, strategic leadership, and commercial acumen, combining deep expertise in Python, .NET, and cloud‑native architectures to deliver scalable, secure, and value-generating intelligent systems.
The MD/D will partner with C‑suite executives, technology leaders, and global delivery teams to embed AI capabilities at scale—accelerating innovation, enhancing decision‑making, and transforming enterprise operations.
What you’ll do:
- Define the global AI & Intelligent Automation strategy, ensuring alignment with enterprise digital transformation and innovation objectives.
- Establish governance frameworks for AI ethics, model transparency, and Responsible AI, ensuring compliance with regulatory and risk standards (e.g., NIST AI RMF, EU AI Act).
- Serve as the senior executive sponsor for AI architecture, operating model, and adoption roadmap.
- Oversee the design and deployment of enterprise‑grade AI solutions using Python, .NET, and cloud‑based MLOps pipelines.
- Direct teams leveraging advanced frameworks including PyTorch, TensorFlow, Hugging Face, ONNX Runtime, and LangChain, integrating orchestration tools like Semantic Kernel, LangGraph, and CrewAI.
- Drive responsible integration of Large Language Models (LLMs) from OpenAI, Anthropic, Google Gemini, and Mistral, including deployment via Azure OpenAI Service or Vertex AI.
- Implement retrieval‑augmented generation (RAG) architectures and manage vector databases such as Pinecone, Weaviate, FAISS, and Milvus to support enterprise knowledge intelligence systems.
- Lead the evolution of the enterprise data estate, leveraging modern data platforms such as Databricks, Snowflake, Azure Synapse, and BigQuery.
- Oversee data engineering using Apache Airflow, dbt, and Prefect, ensuring data pipelines are performant, governed, and aligned with enterprise metadata standards (Collibra, Alation, Microsoft Purview).
- Drive the adoption of Delta Lake, Iceberg, and Hudi for scalable data lakehouse architectures.
- Champion multi‑cloud architecture and engineering excellence across Azure, AWS, and GCP.
- Ensure resilient and cost‑effective deployment via Docker, Kubernetes (AKS/EKS/GKE), and Terraform/Bicep.
- Lead enterprise MLOPs initiatives using Azure ML, SageMaker, Vertex AI, MLflow, and Kubeflow, with continuous integration pipelines (GitHub Actions, Azure DevOps, Jenkins, Argo CD).
- Oversee monitoring and observability using Prometheus, Grafana, ELK/EFK, and OpenTelemetry.
- Guide integration of AI/ML workflows into enterprise‑grade .NET Core applications and service‑oriented architectures.
- Modernize legacy systems through microservices, REST/gRPC APIs, and message‑driven solutions (Azure Service Bus, Kafka).
- Implement secure and compliant DevSecOps practices—SonarQube, Checkmarx, Vault, and Azure API Management—aligned to enterprise standards.
- Drive end‑to‑end intelligent automation using Power Automate, Blue Prism, and Automation Anywhere.
- Integrate cognitive services including Azure Cognitive Services, AWS Comprehend, Form Recognizer, and Speech/Translation APIs to augment digital workflows.
- Lead enterprise process mining and optimization initiatives via Celonis, Power BI Process Mining, and ProcessGold.
- Oversee the integration of analytics and AI to deliver measurable business outcomes.
- Advance enterprise analytics using Power BI, Looker, and Azure Analysis Services.
- Foster data‑driven decisioning through predictive and optimization models using PyCaret, Prophet, and Optuna.
- Ensure alignment with enterprise security standards and frameworks (SOC2, ISO27001, NIST).
Leadership, Talent & Innovation
- Build and lead high‑performing global teams in data science, engineering, and automation disciplines.
- Cultivate a culture of innovation, continuous learning, and responsible experimentation.
- Engage with the external AI ecosystem—academic institutions, hyperscalers, and startups—to identify strategic partnerships and emerging opportunities.
This role is for you if you have:
- Proven record integrating Python‑based AI with .NET enterprise systems.
- Deep expertise across multi‑cloud environments, data governance, and enterprise DevSecOps.
- Demonstrated ability to deliver large‑scale transformation programs and measurable ROI.
- Strong executive presence, communication, and client/stakeholder management skills.
Director of AI Engineering in Tech Catalyst UK in London employer: Dormont Manufacturing Co
Dormont Manufacturing Co is an exceptional employer, offering a dynamic work environment that fosters innovation and collaboration. With a strong commitment to employee growth, the company provides extensive training opportunities and a comprehensive benefits package, all while supporting a flexible hybrid work model that enhances work-life balance. Join us in shaping the future of lottery solutions in a role that promises meaningful impact and professional development.
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We think this is how you could land Director of AI Engineering in Tech Catalyst UK in London
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We think you need these skills to ace Director of AI Engineering in Tech Catalyst UK in London
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
