Consultant - Manager, AI & Data Engineer, AI & Data, Engineering & Transformation

Overview

In this role, you will deliver AI/ML engineering on client projects, building modern analytics platforms and AI solutions. You’ll collaborate with cross-functional teams, coordinate with distributed delivery networks, and translate business needs into scalable AI pipelines. You’ll work at the forefront of AI/data technology, contributing to transformative initiatives for major clients. This opportunity offers strong training, a collaborative culture, and hybrid working in London, with scope to grow technically and professionally.

Pay / Benefits

• training and development opportunities
• hybrid working
• flexible working arrangements
• collaborative culture
• wellbeing focus

Responsibilities

• Collaborate with client stakeholders and internal teams to translate business requirements into AI solutions
• Coordinate with distributed and offshore delivery teams to align plans and outputs
• Design, develop, and deploy end-to-end AI pipelines (data acquisition, preprocessing, feature engineering, model training, evaluation, deployment)
• Develop and implement AI/ML models for use cases such as prediction, automation, and insight generation
• Build and maintain scalable data pipelines, datasets, and data models to support analytics and AI use cases
• Stay updated on AI advancements and evaluate new technologies
• Optimize data processing, storage, and model performance for scalability and efficiency

Key requirements

• Hands-on AI/ML engineering experience in a professional setting
• Experience building data pipelines with structured and unstructured data
• MLOps tools and practices for model deployment, monitoring, governance
• Agile delivery experience
• Strong analytical and problem-solving skills
• Programming: Python, SQL, or similar
• AI/ML Frameworks: TensorFlow, PyTorch, scikit-learn
• Familiarity with Generative AI and Langchain desirable
• ML algorithms, deep learning architectures, statistical modelling
• Cloud platforms with AI/ML services (AWS, Azure, GCP)
• Data engineering: SQL and big data tech (Spark, Hadoop); ETL and data pipelines
• Data governance, security, and privacy standards; metadata management, data quality, lineage
• Distributed computing concepts (parallel processing, streaming, batch orchestration)
• collaborative
• curious
• strong communication
• Python
• SQL
• TensorFlow

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