Senior Backend Engineer (MLOps)

The MLOps team are responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production. Acting as the critical link between the data science and R&D teams, this team will ensure that ML models transition smoothly from development to production, maintaining high availability, scalability, and performance.

Key responsibilities include

• Managing and optimising existing ML model deployments to ensure reliability and efficiency.
• Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management.
• Collaborating closely with data scientists to understand and implement model requirements.
• Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems.
• Implementing robust CI/CD pipelines, monitoring systems, and infrastructure automation.
• Upholding best practices in security, cost management, and infrastructure design for cloud environments.
• This team will play a pivotal role in ensuring that ML initiatives drive value effectively while maintaining operational excellence and we're looking for a Senior Software Engineer to be part of it!

Responsibilities

• Architect and develop robust pipelines for ML model training, testing, and deployment.
• Implement and maintain CI/CD workflows for ML projects.
• Monitor production ML systems for performance, errors, and drift.
• Automate infrastructure provisioning and deployment using IaC tools.
• Collaborate with team leader to define technical strategies.

• 4+ years of experience in backend engineering, systems programming, or high-performance software development roles.
• Strong proficiency in low-level programming languages: Rust, Go, or C/C++ (at least one required, multiple preferred).
• Experience building high-performance, scalable backend systems and APIs.
• Knowledge of systems programming concepts: memory management, concurrency, performance optimization.
• Familiarity with ML system architecture and computational requirements (model serving, training infrastructure, data processing pipelines).
• Experience with cloud platforms (AWS preferred) and distributed systems.
• Proficiency with containerization (Docker) and orchestration tools (Kubernetes).
• Strong experience with version control systems (Git) and CI/CD pipelines.
• Understanding of database systems and data pipeline architectures.
• Ability to troubleshoot and optimize complex production systems under load.
• Experience with monitoring, observability, and performance profiling tools.
• Strong communication and collaboration skills for working with ML researchers and data scientists.

• Python experience for interfacing with ML frameworks
• Experience with real-time systems or low-latency applications
• Knowledge of GPU computing and CUDA
• Background in numerical computing or scientific software