Complexio | Senior DevOps Engineer

Complexio
Bristol
1 year ago
Applications closed

Related Jobs

View all jobs

Azure Data Engineer / BI Developer

Principal Data Engineer (MS Azure)

Data Analyst

Data Analyst

GCP Data Engineer

Data Scientist

Complexio is Foundational AI works to automate business activities by ingesting whole company data – both structured and unstructured – and making sense of it. Using proprietary models and algorithms Complexio forms a deep understanding of how humans are interacting and using it. Automation can then replicate and improve these actions independently.


Complexio is a joint venture between Hafnia and Símbolo, in partnership with Marfin Management, C Transport Maritime, Trans Sea Transport and BW Epic Kosan. 

 

About the job

As a DevOps engineer at our AI product company, you will define and create the platform for deploying, managing, and optimizing our distributed systems across on-premises, multiple cloud environments (AWS, Azure, Google Cloud), and Kubernetes.


Our system leverages multiple LLMs, Graph and Vector Databases and integrates data from multiple sources to power our AI solutions. You will ensure our infrastructure is robust, scalable, and secure, supporting the seamless delivery of our innovative products. This role requires combining cloud technologies and database management expertise, embracing the challenges of integrating AI and machine learning workflows on modern GPUs.


Responsibilities


  • Preferred M.Sc or Ph.d degree in Computer Science or a related field
  • At least 7 years of experience deploying and managing cloud infrastructure (AWS, Azure, Google Cloud) 
  • At least 3 years experience in working with kubernetes environments
  • Proficient in managing and scaling Kubernetes clusters, including monitoring, troubleshooting, and ensuring high availability
  • Experience with cloud-native technologies, CI/CD pipelines, and containerization tools (e.g., Docker)
  • Familiarity with data integration and management from multiple sources in a distributed system environment
  • Proficiency in at least one programming language (Python, Java, Go), and experience with scripting for automation
  • Strong understanding of network infrastructure and security principles, ensuring compliance with data protection regulations


A Bonus:


  • Proficient in database management, specifically with Neo4j and vector databases, including setup, scaling, and optimization for performance and reliability
  • Experience deploying and running Machine Learning Solutions, including LLMs


  • Remote working (Remote must be within 3-5 hours of CET timezone)


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.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.