Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior MLOps Engineer

Bright Purple
Dundee
8 months ago
Applications closed

Related Jobs

View all jobs

DATA SCIENTIST

(INV) Senior Consultant, Data Engineer, AI&Data, UKI

Senior Data Scientist

Data Scientist

Senior Data Scientist - Energy AI & MLOps Leader

Senior Data Engineer

Senior MLOps Engineer - Hybrid/DundeeSalary up to £70,000We are looking for aSenior MLOps Engineerto join a Scottish company working on cutting edge AI solutions. You will play a pivotal role in ensuring that ML initiatives drive value effectively while maintaining operational excellence.The Role:

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.

The Ideal Candidate:4+ years of experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of data engineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems.. If you have the skills and desire for this, then please email your CV to Bright Purple is an equal opportunities employer: we are proud to work with clients who share our values of diversity and inclusion in our industry.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.