GCP AI/ML Engineer

Job Description: The GCP AI/ML Engineer role involves designing and implementing Graph Neural Network architectures for enterprise applications, optimizing vector embedding generation pipelines, and deploying machine learning models using various frameworks. The position requires collaboration with data engineers and solution architects to integrate models into scalable cloud solutions while engaging with customer technical teams to address business needs. The role also includes producing technical documentation and presentations for stakeholders.

Key Responsibilities:

• Design and implement Graph Neural Network (GNN) architectures for enterprise-scale applications.
• Develop and optimize vector embedding generation pipelines using ScaNN or similar ANN techniques.
• Train, fine-tune, and deploy ML/DL models using TensorFlow, PyTorch, JAX, or similar frameworks.
• Collaborate with data engineers and solution architects to integrate models into scalable cloud solutions.
• Perform model evaluations, A/B testing, and hyperparameter tuning for optimal performance.
• Build reusable pipelines and tools for ML training, deployment, and monitoring on GCP.
• Engage directly with customer technical teams to understand business needs and translate them into ML solutions.
• Produce technical documentation and presentations for internal and customer-facing stakeholders.

Skills Required:

• Bachelor’s degree in computer science, Mathematics or a related technical field or equivalent practical experience.
• Certifications: Minimum: Google Professional Data Engineer; Preferred: AWS Machine Learning Specialty Certification.
• 7+ years in a customer facing role working with enterprise clients.
• 4+ years of experience working in enterprise data warehouse and analytics technologies.
• Hands-on experience building and training machine learning models.
• Experience writing software in one or more languages such as Python, Scala, R, or similar.
• Experience working with recommendation engines, data pipelines, or distributed machine learning.
• Experience working with deep learning frameworks (such as TensorFlow, Keras, Torch, Caffe, Theano).
• Strong coding skills in Python and familiarity with ML/DL libraries like TensorFlow, PyTorch, or JAX.
• Knowledge of data analytics concepts, including data warehouse technical architectures, ETL and reporting/analytic tools.
• Customer facing experience of discovery, assessment, execution, and operations.
• Demonstrated excellent communication, presentation, and problem solving skills.
• Experience in project governance and enterprise customer management.
• Proficiency in building Graph Neural Networks (GNNs) using frameworks like DGL, PyTorch Geometric, or similar.
• Experience with ScaNN or other approximate nearest neighbor (ANN) techniques for vector similarity search.
• Hands-on experience with Google Cloud Platform (GCP) tools such as Vertex AI, BigQuery, and Dataflow.
• Strong problem-solving and communication skills, including the ability to work with clients and cross-functional teams.

Salary (Rate): undetermined

City: London

Country: United Kingdom

Working Arrangements: undetermined

IR35 Status: undetermined

Seniority Level: undetermined

Industry: IT