Principal Machine Learning Engineer

Job Description

Compensation

• £80,000 – £100,000 base salary + performance-related bonus + benefits
• Performance-Related Bonus
• Benefits (listed below)

TL;DR

• Role : Principal Machine Learning Engineer
• Location : UK-based, fast-growing technology consultancy specialising in the energy sector
• Cloud Experience : Must have AWS or Azure (certifications are desirable)
• Management : No direct line management required (unless you prefer it)
• Consultancy and/or Energy Experience : Highly beneficial, non-essential
• Visa Sponsorship : Not currently available – you must have right to work in the UK and UK now, and in the foreseeable future
• Flexibility : We consider part-time, condensed hours, job-shares, and other flexible arrangements – please apply even if you only meet part of the requirements, please reach out if you want to know more about the role and if it’s a fit for you
• Diversity : Extremely important to us — we want to see a broad mix of people from all backgrounds

Who We Are

Hypercube Consulting is a rapidly growing data and AI consulting firm dedicated to the energy sector . We combine deep domain expertise with cutting-edge technology to help our clients unlock value from their data. We are building a world-class team of experts eager to tackle challenging problems and shape the future of AI in energy.

To learn more about our philosophy and approach, visit our founder’s blog:

Adam Sroka’s Blog

Role Purpose

We are seeking a Principal Machine Learning Engineer to lead and shape the design, development, and deployment of ML solutions at Hypercube. You will collaborate with data engineering, analytics, and cloud experts to guide our clients’ data strategy and ML roadmap, ensuring they gain tangible value from their data assets.

As an early hire in a fast-growing organisation, you will have considerable influence over technical direction, team culture, and project delivery standards. You will:

• Engage with clients to understand their business challenges and propose ML-driven solutions.
• Architect and implement end-to-end ML pipelines, from data ingestion and feature engineering to model deployment and monitoring.
• Champion best practices in MLOps, model lifecycle management, and cloud-ML services.
• Mentor and upskill colleagues, establishing Hypercube as a hub of ML engineering excellence.

Key Responsibilities

• Technical Leadership & Strategy – Serve as the primary ML subject matter expert and advisor for both internal teams and clients. Define and guide the development of advanced ML solutions, ensuring they align with best practices and modern architectures.
• End-to-End ML Delivery – Design, build, and maintain scalable ML pipelines on AWS or Azure (e.g. Databricks, MLFlow, SageMaker, Azure ML). Oversee the model lifecycle, from exploratory data analysis and feature engineering through to model serving and monitoring in production.
• Collaboration & Stakeholder Management – Work with cross-functional teams (data engineers, DevOps, data scientists, and business stakeholders) to capture requirements and translate them into practical ML solutions. Clearly communicate technical concepts and outcomes to both technical and non-technical audiences.
• Thought Leadership & Evangelism – Advocate for MLOps best practices (CI/CD, infrastructure as code, automated testing, monitoring) within Hypercube and at client sites. Contribute to community outreach by blogging, participating in speaking engagements, or collaborating on open-source initiatives.
• Business Development & Growth – Support pre-sales activities (e.g. demos, proposals) and help shape new projects. Build and maintain strong client relationships, becoming a trusted adviser for their AI strategy. Mentor colleagues, expanding our collective knowledge and capabilities as we scale.

Technical Skills & Experience

Please apply even if you do not meet all of these criteria — we value potential as well as experience.

Core Skills

• LLMs & Generative AI : Practical experience with large models.
• Cloud ML Experience (AWS or Azure) : Proven track record of deploying and managing ML workloads in a production environment.
• Advanced Python : Expertise in building ML pipelines and writing efficient, maintainable code.
• MLOps & Model Management : Experience with tools such as MLFlow, Kubeflow, Airflow/Prefect for scheduling, or similar platforms for model tracking and deployment.
• Data Processing : Proficiency with Spark or Databricks for large-scale data processing.
• SQL : Strong background in querying, wrangling, and optimising data.
• Data Architecture : Familiarity with data lake, data warehouse, or lakehouse architectures (e.g. Delta Lake).

Additional (Nice-to-Have) Skills

• Infrastructure as Code : Terraform or similar.
• Containers & Kubernetes : Docker, EKS/AKS, Container Registries.
• Streaming Technologies : Kafka, Kinesis, or Event Hubs.
• Certifications in AWS or Azure.

Other Desirable Experience

• Consultancy or Energy Sector background.
• Public Thought Leadership : Blogging, conference talks, YouTube content, open-source contributions.
• Stakeholder Management : Translating business requirements into technical solutions and vice versa.
• Complex Integrations : Experience integrating with external or on-premises systems in a hybrid cloud setup.
• Excellent Communication : Able to convey complex technical topics clearly to varied audiences.

What’s in It for You?

• High-Impact Role : Shape data and AI strategies in the energy sector, directly influencing client success.
• Career Progression : Work closely with seasoned data leaders; benefit from an events budget and mentorship programmes .
• Flexible Work : We’re open to part-time, condensed hours, job-shares — just ask!
• Start-Up Environment : As an early senior hire, you will help shape our culture, processes, and technology decisions.
• Personal Branding : We support blogging, speaking engagements, and open-source work to help you elevate your professional profile.

Benefits

• Performance-Related Bonus
• Enhanced Pension
• Enhanced Maternity/Paternity
• Private Health Insurance
• Health Cash Plan
• Peer Cash Award Scheme
• Cycle-to-Work Scheme
• Flexible Working (remote/hybrid options)
• Events & Community involvement
• EV Leasing Scheme
• Training & Events Budget
• Mentorship Programmes

Diversity & Inclusion

Hypercube is committed to creating a diverse and inclusive environment , which reflects our broader society. We encourage applications from candidates of all backgrounds and experiences. All qualified applicants will receive consideration without regard to race, gender, reassignment, marital or civil partnership status, pregnancy/maternity, sexual orientation, or belief.

We are open to part-time, condensed hours, job-shares , or other flexible arrangements to attract the very best talent. If you have particular requirements, please let us know, and we will do our best to accommodate them.

Ready to Apply?

If this sounds like you — or you meet some but not all of the requirements — we would love to hear from you . Please apply via our careers page or reach out directly. We look forward to discovering how your experience can help shape our mission to transform data and AI in the energy sector!

N.B. We are currently not able to sponsor visas.

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