Machine Learning Engineer (London)

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Our SAIL (Solutions and AI Lab) practice is looking for an experienced Machine Learning Engineer to join the team.

In SAIL, we build state-of-the-art AI solutions that help our clients with some of their biggest projects ranging from tools that support energy network risk forecasting and climate change adaptation using empirically-derived resilience models, to satellite and aerial imagery-based image recognition software, and genAI-powered applications including bespoke assistants and agents.

We focus on delivering value-adding solutions tailored to our clients specific needs, across sectors such as Financial Services, Products & Services, Energy & Resources, Pharmaceutical & Lifesciences, and Government.

Curious about our impact? Check out our case studies to see how we accelerated low-carbon device roll-outs in the UK.

What you will be doing

You will leverage your experience to help our clients solve their key data challenges. You will also support the growth of our team by helping them develop the necessary skills. Typical engagements include:

• Defining and implementing Machine Learning projects throughout their lifecyclefrom conception, data preparation, model engineering, evaluation, deployment, to monitoring and maintenance.
• Establishing and developing ML Ops frameworks and standards for clients, integrating them into their infrastructure.
• Working with clients to guide them through their ML journey, upskilling them and ensuring they can take ownership post-project.
• Performing maturity assessments of clients Cloud/AI environments and recommending improvements.
• Building ML strategy blueprints and advising on technology options.
• Translating business requirements into solutions, ensuring compliance with organizational policies and standards, and helping define new policies and philosophies when needed.
• Helping clients identify risks and mitigations for ML and data science programs, including transitioning from on-premises to cloud-based infrastructures (AWS, Azure, GCP).
• Working on ML model governance topics, including fairness, transparency, interpretability, and accountability.

Your skills and experience

We seek passionate ML engineers eager to build production solutions and contribute to our company's growth. You should be capable of advising clients and actively participating in technical delivery. Your skills include:

• Passion for solving machine learning problems, with a blend of technical delivery and consulting skills.
• Advanced degree in computer science, mathematics, physics, engineering, or related STEM field.
• Strong problem-solving skills with a solid foundation in classical ML and deep learning, from statistics and traditional algorithms to transformers and state-of-the-art models.
• Excellent collaboration and communication skills, both within teams and with clients.
• Interest in building AI applications such as forecasting tools, image recognition, and LLM-based chatbots and agents.
• Proven ability to develop ML models and pipelines using Python and libraries like PyTorch and TensorFlow, from conception to deployment in scalable environments.
• Ability to design, deploy, and maintain ML solutions on modern frameworks, adhering to best practices, including version control, testing, MLOps, CI/CD, and API design.
• Hands-on experience with at least one major cloud platform (AWS, Azure, GCP) in a production setting, and familiarity with ML platforms like AWS Sagemaker or Azure Machine Learning Studio.
• A commitment to continuous learning and skill development, and the ability to foster growth in others.

We recruit individuals at all levels based on merit. Dont worry about fitting into a quotaif you have the skills, we want to talk to you.

What a career at Baringa will give you

Putting People First.

Baringa is a People First company that values wellbeing, work-life balance, and flexible working. Our benefits include:

• Generous annual leave: 5 weeks plus an additional 2 weeks after 5 years of service.
• Flexible working arrangements and hybrid policies.
• Corporate Responsibility Days: 3 days annually for social and environmental causes.
• Wellbeing Fund: Support for personal wellbeing activities.
• Career Progression: Quarterly promotion reviews without quotas.
• Profit Share Scheme: Employees share in the company's success.

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