Machine Learning Engineer

Datatonic; London Borough of Harrow; Hybrid; GBP 65,000 - 90,000; Full time

Benefits offered by this job

Holiday: 25 days + bank holidays

Private health insurance

Gym membership discounts

Hybrid work model with WFH allowance

Udemy learning platform access

Auto-enrolment pension after probation

Life Insurance

Income Protection

Cycle to Work Scheme

Tech Scheme

Job summary

Datatonic is shaping the future of AI and data with Google Cloud expertise, driving transformation for clients through ML, data engineering, and analytics. This role focuses on building production-grade ML solutions, with opportunities to lead projects and client discussions.

We seek engineers who combine strong ML fundamentals with excellent communication to deliver innovative AI & cloud-based solutions that meet real business needs. Hybrid work and extensive learning benefits are offered.

Qualifications

• 1-3 years as a Machine Learning Engineer, preferably with a consulting background.
• Proficiency in Python as a backend language, able to deliver production-ready code in CI/CD pipelines.
• Familiarity with cloud platforms such as Google Cloud, AWS, or Azure.
• Foundational software engineering practices.
• Strong knowledge of SQL for data querying and management.
• Experience scaling computations using GPUs or distributed computing.
• Experience exposing ML components through web services or wrappers (Flask).

Responsibilities

• Translating Requirements: Interpret vague requirements and develop models to solve real-world problems.
• Data Science: Conduct ML experiments using programming languages with machine learning libraries.
• GenAI: Leverage generative AI to develop innovative solutions.
• Optimisation: Optimise machine learning solutions for performance and scalability.
• Custom Code: Implement tailored machine learning code to meet specific needs.
• Data Engineering: Ensure efficient data flow between databases and backend systems.
• MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.
• ML Architecture Design: Create machine learning architectures using Google Cloud tools and services.
• Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.

Skills

Python

CI/CD

SQL

MLOps

Cloud platforms

Data science

Communication

Tools

Flask

Google Cloud

AWS

Azure

Git

Job description

Shape the Future of AI & Data with Us

At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world.

Your Mission

As a Machine Learning Engineer, you'll know how to engineer beautiful code in Python and take pride in what you produce. You'll be an advocate of high-quality engineering and best-practice in production software as well as rapid prototypes.

Whilst the position is a hands-on technical role, we'd be particularly interested to find candidates with a desire to lead projects and take an active role in leading client discussions. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements.

To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You'll be comfortable with model optimisation and deployment tools and practices. Furthermore, you'll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud.

What You’ll Do

• Translating Requirements: Interpret vague requirements and develop models to solve real-world problems.
• Data Science: Conduct ML experiments using programming languages with machine learning libraries.
• GenAI: Leverage generative AI to develop innovative solutions.
• Optimisation: Optimise machine learning solutions for performance and scalability.
• Custom Code: Implement tailored machine learning code to meet specific needs.
• Data Engineering: Ensure efficient data flow between databases and backend systems.
• MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.
• ML Architecture Design: Create machine learning architectures using Google Cloud tools and services.
• Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.

What You’ll Bring

• Experience: 1-3 years as a Machine Learning Engineer, preferably with a consulting background.
• Programming Skills: Proficiency in Python as a backend language, capable of delivering production-ready code in well‑tested CI/CD pipelines.
• Cloud Expertise: Familiarity with cloud platforms such as Google Cloud, AWS, or Azure.
• Software Engineering: Hands‑on experience with foundational software engineering practices.
• Database Proficiency: Strong knowledge of SQL for querying and managing data.
• Scalability: Experience scaling computations using GPUs or distributed computing systems.
• ML Integration: Familiarity with exposing machine learning components through web services or wrappers (e.g., Flask in Python).
• Soft Skills: Strong communication and presentation skills to effectively convey technical concepts.

Bonus Points If You Have

• Scale‑up experience.
• Cloud certifications (Google CDL, AWS Solution Architect, etc).

What’s in It for You?

We believe in empowering our team to thrive, with benefits including:

• Holiday: 25 days plus bank holidays (obviously!)
• Health Perks: Private health insurance (Vitality Health) and Smart Health Services
• Fitness & Wellbeing: 50% gym membership discounts (Nuffield Health, Virgin Active, Pure Gym).
• Hybrid Model: A WFH allowance to keep you comfortable.
• Learning & Growth: Access to platforms like Udemy to fuel your curiosity.
• Pension: (Auto-enrolment after probation period. 3% employer contributions raising 1% per year of service to a max of 10%)
• Life Insurance: (3 x your base salary!)
• Income Protection: (up to 75% of base salary, up to 2 years)
• Cycle to Work Scheme
• Tech Scheme

Why Datatonic?

Are you ready to make an impact?

Join us to work alongside AI enthusiasts and data experts who are shaping tomorrow. At Datatonic, innovation isn’t just encouraged - it’s embedded in everything we do. If you’re ready to inspire change and deliver value at the forefront of data and AI, we’d love to hear from you!