Software Engineer delivering MLOps solutions for Generative AI at DataGalaxy. Focusing on reliability and collaboration with product engineering teams in a hybrid environment.
About the role
- Machine Learning Engineer developing LLM-powered systems at Trainline. Designing predictive ML systems, collaborating with cross-functional teams on AI initiatives.
Responsibilities
- Design and build LLM-powered agentic systems using frameworks such as LangGraph and LangChain
- Develop and optimise RAG pipelines, tool-using agents, and multi-step workflows with appropriate guardrails and validation
- Monitor and evaluate model and agent performance using appropriate ML metrics (e.g. precision, recall) and production monitoring tools
- Maintain and improve traditional ML models alongside newer GenAI capabilities
- Partner closely with stakeholders to frame problems, define success metrics, and deliver measurable business impact
- Take ownership of technical initiatives, driving delivery from ideation through to production and iteration
- Contribute to our wider AI & ML community through knowledge sharing, experimentation, and continuous learning
Requirements
- Strong experience in Machine Learning with solid foundations in the ML lifecycle, evaluation methodologies, and statistical thinking
- Hands-on experience with GenAI, Large Language Models, and NLP techniques, including RAG and agent-based systems
- Proficient in Python and common ML libraries (e.g. PyTorch, scikit-learn, XGBoost, Pandas)
- Experience deploying and operating ML or AI systems in production environments
- Understanding of ML Ops and DevOps principles (e.g. Docker, CI/CD, infrastructure as code)
- Experience working with cloud infrastructure (preferably AWS)
- Take ownership of technical tasks and proactively drive solutions forward
Benefits
- Private healthcare & dental insurance
- Generous work from abroad policy
- 2-for-1 share purchase plans
- EV Scheme to further reduce carbon emissions
- Extra festive time off
- Excellent family-friendly benefits
- Clear career paths
- Transparent pay bands
- Personal learning budgets
- Regular learning days
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Senior Machine Learning Engineer responsible for designing, building, and deploying ML solutions. Joining a global tech group tackling high - impact projects in Buenos Aires.
Principal Machine Learning Engineer at Qodea responsible for leading ML model lifecycle and collaborating on AI solutions in Buenos Aires delivery center.
Lead ML Ops Engineer for a fast - growing AI startup focused on scalable infrastructure. Drive hands - on execution across the entire model lifecycle in a collaborative environment.
Lead Machine Learning Engineer creating personalized item recommendations for Target.com and the Target App. Designing and optimizing production ML solutions with a team of data scientists and engineers.
Senior Machine Learning Engineer at Doctrine focusing on developing NLP models for legal document processing. Join an ambitious team to innovate within the field of legal technology.
MLOps Engineer responsible for designing and maintaining ML pipelines at JobCloud. Collaborating with teams to productionize ML models and ensuring robust system performance.
Senior ML Engineer developing scalable production ML systems across various teams in JobCloud. Leading innovation in the AI - driven recruitment landscape, improving job ad visibility and performance.
Senior Machine Learning Engineer at greehill developing ML solutions for sustainable urban living. Leading projects in Computer Vision and Deep Learning to transform urban environments.
Machine Learning Engineer developing deep learning models for self - driving vehicle systems at BlueSpace.ai. Engage in innovative ML applications while collaborating with a seasoned team in the autonomous vehicle ecosystem.