Backend Engineer

Founding Engineer - MLOps & Data Engineering

We're working with a venture backed technology company that is building software to solve complex challenges within the natural resources sector. Following recent funding and growing customer demand, they are looking to hire a Backend Engineer with strong MLOps and cloud engineering expertise to help scale their platform, machine learning infrastructure, and data capabilities.

This is an opportunity to join a small, high performing team where you'll have significant ownership over technical decisions, architecture, and the systems that underpin cutting edge AI and data driven products.

The Role

The successful candidate will play a key role in designing, building, and operating the backend systems, machine learning infrastructure, and cloud platforms that power the company's products.

Key responsibilities include:

• Designing and building scalable backend services, APIs, and distributed systems using Python
• Developing and maintaining production grade machine learning workflows and data pipelines
• Building infrastructure to support model training, deployment, monitoring, and lifecycle management
• Managing and improving cloud infrastructure, primarily within Azure
• Working closely with Data Scientists to productionise machine learning models and AI driven products
• Implementing MLOps best practices, including automated testing, deployment, monitoring, and observability
• Designing robust data ingestion and processing pipelines for large and complex datasets
• Improving platform reliability, scalability, and performance across backend and ML systems
• Driving engineering best practices around architecture, CI/CD, infrastructure as code, and deployment workflows
• Taking ownership of projects from concept through to production deployment

Requirements

• 5+ years of experience building and operating production software systems
• Strong software engineering experience with Python
• Experience building and maintaining machine learning workflows or MLOps platforms
• Strong understanding of cloud infrastructure, preferably Azure
• Experience with containerisation and orchestration technologies such as Docker and Kubernetes
• Experience building APIs, backend services, and distributed systems
• Strong understanding of modern software architecture and engineering best practices
• Experience working with data intensive applications, ETL pipelines, or large scale datasets
• Familiarity with CI/CD pipelines and infrastructure as code
• Strong organisational skills and ability to prioritise effectively
• Comfortable working in a fast moving startup environment
• Proactive mindset with a desire to improve systems, processes, and products

Desirable Experience

• Experience deploying and operating machine learning models in production
• Experience with ML orchestration tools such as Airflow, Prefect, Kubeflow, MLflow, or similar
• Exposure to geospatial data, scientific computing, environmental modelling, or natural resources applications
• Experience with observability, monitoring, and platform engineering practices
• Experience working with LLMs, AI systems, or modern machine learning frameworks
• Strong DevOps and Site Reliability Engineering experience
• Experience working within venture backed startups or scale ups

What's on Offer

• Opportunity to join a well funded, high growth technology business
• Significant influence over technical direction, platform architecture, and ML infrastructure strategy
• Work alongside experienced engineers, researchers, data scientists, and domain experts
• Exposure to cutting edge applications of AI, machine learning, and scientific data
• Competitive salary and equity package