Machine Learning Engineer, AI
Summary
Research and prototype AI models (generative, adversarial) for UK government projects, using Python, PyTorch, and Docker to build proofs of concept.
- Work on exciting public sector projects and make a positive difference in people’s lives.
- Research machine learning models and evaluate their application within the specialist domain.
- Collaborate with a small team to deliver AI capabilities for the UK government.
- Keep learning about various AI techniques such as classical ML, generative, and adversarial methods.
- Experiment with new approaches and translate them into practical capabilities.
Requirements
- Model Research & Evaluation: Research emerging machine learning models and techniques, and assess how they could be applied within our specialist domain.
- Applied AI Research: Turn research into practical proofs of concept, exploring generative, adversarial and other AI approaches.
- Domain Application: Work with the team to understand our customers problems in depth and translate ML capability into solutions that fit their specialist domain.
- Continuous Learning: Keep pace with the fast moving field of AI building knowledge across different model types and techniques and sharing what you learn with the team.
- Research Skills: Comfortable reading and evaluating ML research and translating findings into practical ideas worth testing.
- Technology Implementation: Ability to evaluate new AI technologies generative, adversarial or otherwise for relevance and feasibility within the UK Government Domain.
- Advanced Prototyping: Ability to build proof of concept applications that bridge the gap between initial research and real-world application.
- Specialist Advice: Keen to grow into a technical resource for the practical application of AI within the organisation, sharing knowledge as your expertise builds.
- Data Science: Applying data science techniques to support model research, evaluation, and refinement.
- Team Fit: A quick learner who's easy to work with, and will slot naturally into a close-knit, high-performing team.
- Technical Expertise: Solid understanding of machine learning algorithms and frameworks with a general grounding in software engineering practices and a genuine curiosity about how ML models work.
- Python: Strong proficiency and hands-on programming experience in Python are required.
- ML Frameworks: Practical experience working with machine learning frameworks (e.g. Pytorch) to build, train, evaluate, and deploy models.
- Version Control: Proficiency with Git for version control and collaborative development is required.
- Containerisation: Familiarity with Docker is required to package, deploy, and run applications across environments.
- Frontend Development: Experience with frontend technologies, specifically JavaScript and React, to help build interactive user interfaces for our prototypes (nice to have).
- Cloud Services: Experience working with cloud platforms (e.g., AWS, Azure, GCP) (nice to have).
- Systems/Backend Languages: Familiarity with other languages used for high-performance backend or ML workloads, such as Rust or C++ (nice to have).
Core Competencies
Demonstrates expertise in machine learning model research and evaluation, with a strong proficiency in Python and experience in applying AI techniques within the public sector. Capable of building proof of concept applications and collaborating effectively within a high-performing team.
Highest-signal resume keywords
- Machine Learning Research
- Python Programming
- ML Frameworks (Pytorch)
- Data Science Techniques
- Version Control (Git)
ATS Optimization Keywords
Hard Skills
- Machine Learning Algorithms
- Generative AI Techniques
- Adversarial AI Techniques
- Advanced Prototyping
- Data Science
- Model Evaluation
- AI Technology Evaluation
- Software Engineering Practices
- Cloud Platforms (AWS, Azure, GCP)
- Systems/Backend Languages (Rust, C++)
Soft Skills
- Continuous Learning
- Team Collaboration
- Adaptability
- Problem-Solving
- Communication
Industry Keywords
- Public Sector Projects
- AI Capabilities
- Proofs of Concept
- Specialist Domain
- Machine Learning Techniques
Tools & Technologies
- Pytorch
- Docker
- Git
- JavaScript
- React