Senior Machine Learning Engineer

Overview

You will design and deliver production-grade ML solutions, primarily in Python, for clients as part of a consulting-style technical delivery team. You’ll balance rapid prototyping with high-quality engineering and best practices, while leading project conversations with clients. You’ll apply ML and data science fundamentals, optimize models, and deploy solutions on Google Cloud, driving impact and measurable business outcomes. This role combines hands-on technical work with opportunities to guide engagements and shape client outcomes. Join a team that values curiosity, collaboration, and delivering innovative AI-enabled results.

Pay / Benefits

• 25 days holiday plus bank holidays
• Private health insurance
• Gym membership discounts
• Hybrid model with WFH allowance
• Pension auto-enrolment with employer contributions
• Life insurance

Responsibilities

• Interpret vague requirements and translate them into ML models addressing real-world problems
• Conduct ML experiments using relevant programming languages and ML libraries
• Leverage GenAI to develop innovative solutions
• Optimize ML solutions for performance and scalability
• Implement tailored machine learning code to meet specific needs
• Ensure efficient data flow between databases and backend systems
• Automate ML workflows with emphasis on testing, reproducibility, and feature/metadata storage
• Design ML architectures using Google Cloud tools and services
• Build and deploy production-grade software for ML and data-driven solutions
• Lead client discussions, scope projects, and oversee delivery of engagements

Key requirements

• 4+ years of experience as a Machine Learning Engineer, preferably with consulting background
• Proficiency in Python for backend development and production-ready code in CI/CD pipelines
• Familiarity with cloud platforms such as Google Cloud, AWS, or Azure
• Hands-on software engineering practices
• Strong SQL knowledge for data querying and management
• Experience scaling computations using GPUs or distributed systems
• Familiarity with exposing ML components via web services or wrappers (e.g., Flask in Python)
• Strong communication and presentation skills

• Strong communication
• Presentation skills
• Client-facing collaboration
• Python programming
• ML libraries and experimentation
• GenAI capabilities

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