Consultant – Manager, AI & Data Engineer, AI & Data, Engineering & Transformation, London

Job Description

As a practitioner in AI & Data, you are responsible for delivering AI/ML Engineering on client projects. You will devise innovative solutions to help clients address their biggest data challenges, including developing modern analytics platforms.

AI/ML Engineers develop, deploy, and maintain AI/ML systems, leveraging ML algorithms, deep learning techniques, and cloud infrastructure. They monitor and optimise model performance.

Responsibilities

• Collaborate with client stakeholders and internal teams to understand business requirements and translate them into robust AI solutions.
• Coordinate with distributed delivery teams across Deloitte’s wider network.
• Design, develop, and implement end-to-end AI pipelines, including data acquisition, preprocessing, feature engineering, model training, evaluation, and deployment.
• Develop and implement AI/machine‑learning models for prediction, classification, automation, and insight generation.
• Build and maintain scalable data pipelines, datasets, and data models to support analytics, reporting, and AI use cases.
• Keep up to date with advances in AI, exploring and evaluating new technologies and approaches.
• Optimise data processing, storage, and model performance for scalability and efficiency.

Qualifications

• Hands‑on experience developing and deploying AI solutions in a professional setting.
• Experience building data pipelines and working with structured and unstructured data.
• Proficiency with MLOps tools and practices for model deployment, monitoring, and governance.
• Experience in agile delivery environments.
• Strong analytical and problem‑solving skills with attention to detail.
• Proficient programming in Python, SQL, and/or similar languages.
• Experience with AI/ML libraries such as TensorFlow, PyTorch, scikit‑learn; familiarity with Generative AI or langchain is desirable.
• Solid understanding of ML algorithms, deep learning architectures, and statistical modelling.
• Experience with cloud platforms (AWS, Azure, GCP) and their AI/ML services.
• Data engineering skills: SQL, big‑data technologies (Spark, Hadoop), ETL, automated workflows.
• Knowledge of data governance, security, and privacy standards.
• Knowledge of distributed computing techniques such as parallel processing, streaming, and batch orchestration.

Benefits & Working Conditions

Location: London. Hybrid working model with flexibility across office, virtual collaboration spaces, client sites, and remote work.

Access to world‑class training, development opportunities, and a supportive collaborative culture.

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