Director of AI Engineering

PWC; Greater London; On-site; GBP 100,000 - 150,000; Full time

Job summary

A global consulting firm based in Greater London is seeking a Director for AI & Intelligent Automation. This strategic role involves defining and executing the enterprise strategy for AI, Machine Learning, and Automation. The ideal candidate will leverage their deep technical expertise in Python and .NET, leading high-performing teams to deliver scalable intelligent systems. This position requires strong leadership skills, a deep understanding of data governance, and the ability to drive large-scale transformation initiatives across multiple cloud environments.

Benefits

Flexible working hours

Health benefits

Professional development opportunities

Qualifications

• Proven record integrating Python-based AI with .NET enterprise systems.
• Deep expertise in multi-cloud environments and data governance.
• Strong executive presence, communication, and client management skills.

Responsibilities

• Define global AI & Intelligent Automation strategy aligned with digital transformation.
• Oversee design and deployment of enterprise-grade AI solutions.
• Lead enterprise MLOps initiatives to enhance automation.

Skills

Python

.NET

Cloud-native architectures

AI Ethics

Commercial acumen

Tools

Azure OpenAI Service

Docker

Kubernetes

Power BI

MLflow

Job description

Role Overview

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 – 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.

Key Leadership Responsibilities

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.

Enterprise AI & GenAI Ecosystem – but not exhaustive or limited by

• 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.

Data Platform & Engineering Excellence

• 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.

Cloud, Infrastructure & MLOps

• 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.

Enterprise Integration with .NET Ecosystems

• 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.

Intelligent Automation & Cognitive Services

• 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.

Analytics, BI, and Decision Intelligence

• 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.

Security, Compliance & Responsible AI

• 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.

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.

Preferred Background

• 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.