Data Engineer (ML) – Gloucester – National Security

at  BAE Systems

LOCATION(S): UK, EUROPE & AFRICA : UK : GLOUCESTER

BAE Systems Digital Intelligence is home to 4,500 digital, cyber and intelligence experts. We work collaboratively across 10 countries to collect, connect and understand complex data, so that governments, nation states, armed forces and commercial businesses can unlock digital advantage in the most demanding environments.
We are looking for experienced Data Engineers to join our team following continuous growth and success in the UK Government sector.
Our people are what differentiates us, they are resourceful, innovative and dedicated. We have a mix of generalists and specialists and recognise that this diversity contributes to our success. We recognise the benefits of forming teams from a mix of disciplines, which allows us to come up with cutting edge, high quality solutions.
Our breadth of work across the Public Sector provides diverse opportunities for our people to develop their careers in new areas of expertise and with new clients. Our projects are spread across the Data Science lifecycle from delivering Research and Proof of Concept models, to building and delivering operational solutions.
Machine Learning (ML) Engineers are responsible for designing, implementing, deploying, and maintaining artificial intelligence (AI) systems. They work closely with data scientists to understand data requirements, clean and organize data, and build efficient, scalable capabilities. The role requires a strong foundation in software engineer, statistics, and AI/ML concepts. Due to the fast-paced change in AI/ML, ML Engineers are expected to keep aware with the latest advancements in artificial intelligence and machine learning technologies, ensuring that solutions are both innovative and effective. Collaboration with cross-functional teams is essential to integrate ML models into larger systems and applications. Knowledge of DevOps and MLOps is needed to ensure they can integrate with wider delivery teams.

• Engineer and implement machine learning (ML) solutions: Able to own the end-to-end process and lifecycle of ML systems.
• Deploy models and capabilities: Handle the technical aspects of bringing models into a production environment.
• Research new techniques: Continuously explore the latest ML and AI advancements to identify methods that can enhance current systems.
• Collaborate with data scientists: Work closely with data scientists to refine, optimise, and implement models based on prototypes.
• Integrate models with delivery teams: Work as part of a wider delivery team to incorporate models into systems we design and deliver.
• Data management: Ensure the quality and accessibility of data used for machine learning projects is appropriate, collaborating with data engineers as necessary.
• Data analysis: Perform analysis of large datasets to uncover trends, patterns, and insights that inform model development and deployment.
• Monitor model performance: Regularly evaluate deployed models, identifying performance gaps and opportunities for optimisation.
• Define and optimize MLOps processes: Create and refine the model development and deployment strategy for better efficiency and results
• Adhere to policy and ethical AI standards: Ensure all machine learning practices comply with policy processes and guidelines.
• Cross-functional technical guidance: Guide cross-functional teams in the implementation and integration of machine learning projects.
• Innovate and prototype: Develop and deploy ML prototypes within the business, testing feasibility and impact.
• Cloud Skills and Expertise: Use cloud-based environments to facilitate scalable machine learning model development and deployment across the MLOps lifecycle.
• Cloud ML Services: Able to integrate pre-built cloud capabilities into a system.