Director of AI Engineering in Tech Catalyst UK
Indexed description
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 – leveraging the latest in thinking in the future agentic web.
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
Strategic Vision & Governance
- 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.
- Ensure high-quality, compliant data foundations for machine learning and analytics workloads.
- 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).
- Oversee identity and access management through Azure AD, OAuth2, OpenID Connect, and integration with enterprise IAM systems.
- Champion ethical AI, bias detection, and explainability through Azure Responsible AI Dashboard and equivalent frameworks.
- 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.
- 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.
We offer a range of benefits including empowered flexibility and a working week split between office, home and client site; private medical cover and 24/7 access to a qualified virtual GP; six volunteering days a year and much more.
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