Developing solutions for MLOps platform at Itaú, the largest bank in Latin America. Collaborating with software engineering practices to enhance user experience in machine learning.
About the role
- Machine Learning Engineer working with multidisciplinary teams to deploy AI/ML systems in Defence. Engaging with clients, troubleshooting issues, and enhancing machine learning solutions.
Responsibilities
- Moving models from research/prototype to live, high-impact production environments, adapting those solutions to client-specific data, systems, and interfaces.
- Resolving unforeseen edge-cases and challenges, providing on-site fixes or relaying them back to the product team.
- Troubleshooting integration issues with existing systems.
- Working directly with product teams to maintain deep technical expertise in Mind Foundry's products, capabilities and workflows.
- Engaging directly with defence customers to translate their needs and goals into technical requirements.
- Providing hands on support to end users.
- Extending and improving internal ML platforms, tooling, and best practices, incorporating learnings from deployments back into shared frameworks.
Requirements
- A Degree in Computer Science, Applied Mathematics, Statistics, Physics, or a related STEM field (or equivalent practical experience)
- Strong engineer with demonstrated proficiency in programming languages such as Python, producing clean, reproducible, well-tested, and well-documented code suitable for long-term ownership and handover.
- Hands-on experience with production infrastructure, including Docker, Linux, CI/CD, MLOps, cloud platforms, and model serving architectures.
- Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
- Exceptional problem-solving skills and
- Comfortable solving technical problems with limited internet access.
- Prior experience working with government customers, defence contractors, or in military environments.
- Experience in areas of model development, data processing and streaming (Spark, Kafka), microservices in python (Flask or FastAPI), and interactive visualisations and User Interfaces (Streamlit, Plotly, Gradio etc).
- Broader software engineering experience (e.g. Java, Node.js, React, PostgreSQL, system architecture, DevOps).
Benefits
- Hybrid working
- Flexible hours
- Professional and personal development
- 25 days of annual leave (plus Bank Holidays and a company-wide break over Christmas)
- Salary Sacrifice Pension scheme with a 5% employer contribution (5% employee contribution)
- Private Healthcare (including dental and optical cover)
- Group Life Cover at three times your annual salary once you pass your probation period
- Enhanced Parental and Sickness Leave
- Workplace Nursery Scheme
- Dog-friendly office!
Job title
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