Machine Learning Engineer

You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high-impact problems in the Defence and National Security arena.

You are engineering-focused, with a keen interest and working knowledge of operationalised machine learning. You have a desire to take cutting‑edge ML applications into the real world. You will develop new methodologies and champion best practices for managing AI systems deployed at scale, with regard to technical, ethical and practical requirements. You will support both technical, and non‑technical stakeholders, to deploy ML to solve real‑world problems.

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• Build software and infrastructure that leverages Machine Learning.
  
• Create reusable, scalable tools to enable better delivery of ML systems.
  
• Work with customers to understand their needs.
  
• Collaborate with data scientists and engineers to develop best practices and new technologies, and
  
• Implement and develop the company's view on what it means to operationalise ML software.
  

As a rapidly growing organisation, roles are dynamic and subject to change. Your role will evolve alongside business needs, but you can expect your key responsibilities to include:

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• Work in cross‑functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high‑impact systems.
  
• Work with senior engineers to scope projects and design systems.
  
• Provide technical expertise to our customers.
  
• Technical Delivery.
  

You can view our company principles here. We look for individuals who share these principles and our excitement to help our customers reap the rewards of AI responsibly.

To succeed in this role, you'll need the following - these are illustrative requirements and we don't expect all applicants to have experience in everything (70% is a rough guide):

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• Understanding of, and experience with the full machine learning life cycle.
  
• Experience deploying trained machine learning models into production environments.
  
• Experience with models developed using common frameworks such as Scikit‑learn, TensorFlow, or PyTorch.
  
• Experience with software engineering best practices and developing applications in Python.
  
• Experience with cloud architecture, security, deployment, and open‑source tools ideally with one of the major cloud providers (AWS, GCP, or Azure).
  
• Experience with containers, Docker and Kubernetes.
  
• Understanding of core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques.
  
• Experience managing/mentoring junior members of the team.
  
• Outstanding verbal and written communication.
  
• Excitement about working in a dynamic role with autonomy and ownership.
  

We like people who combine expertise and ambition with optimism and who are interested in changing the world for the better.

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• Think scientifically; test assumptions, seek evidence, always look for opportunities to improve.
  
• Love finding new ways to solve old problems; never settle for ‘good enough’.
  
• Are pragmatic and outcome‑focused; balance the big picture with details and see ideas executed in the real world.
  

The company's team is diverse and distinctive, and we all come from different professional and organisational backgrounds. We all share curiosity that powers us forward each day.

The company is the professional challenge of a lifetime. You'll be surrounded by an impressive group of brilliant minds working to achieve our collective goals.

Our consultants, product developers, business development specialists, operations professionals, and more all bring something unique to the company, and you'll learn something new from everyone you meet.

People Source Consulting Ltd is acting as an Employment Agency in relation to this vacancy. People Source specialise in technology recruitment across niche markets including Information Technology, Digital TV, Digital Marketing, Project and Programme Management, SAP, Digital and Consumer Electronics, Air Traffic Management, Management Consultancy, Business Intelligence, Manufacturing, Telecoms, Public Sector, Healthcare, Finance and Oil & Gas.