Machine Learning Engineer · ·

FXC Intel
London
2 weeks ago
Create job alert

MLOps Engineer

While professional experience and qualifications are key for this role, make sure to check you have the preferable soft skills before applying if required.European Remote$5,000 to $6,500 per monthWe are looking for a MLOps Engineer to join our DataOps team, a new and growing team within FXC Intelligence with a focus on being the intermediary between Data Platform and DevOps teams, supporting our AWS migration and working closely with the AI team.What you’ll be working on:Building and maintaining our data infrastructure using DevOps and Data Engineering practices, prioritising the needs of stakeholdersCollaborate with Data Practitioners across the company to gain an understanding of their pains and needs and support them where engineering or data science experience is requiredHelp Data Scientists and ML Engineers write reliable code and ship it to clientsHelp Data Analysts and other people in the business by providing the necessary tools and processesCollaborate with the DevOps team regarding standards and best practices for working with infrastructure in the companyParticipate in the migration from on-prem to AWS in the area of data infrastructureCollaborate with the evolution of the data stack, focusing on scalability, reliability and transparencyAbout the DataOps team:The DataOps team is a new and growing team within FXC, serving as a critical intermediary between the Data Platform team and DevOps, focusing on implementing core functionalities for databases and ETL/ELT toolsThe team plays a key role in the migration of infrastructure to AWS, ensuring efficiency and scalabilityDataOps also collaborates closely with the AI team to develop and maintain machine learning pipelines, supporting the deployment and management of AI modelsYou should apply if you have:Experience with deploying, testing and monitoring ML modelsExperience with data orchestration/pipelines and data warehousingGood working knowledge of Python and data science librariesOperational familiarity with ML Infrastructure tools such as Kubeflow, MLFlow and neptune.aiAn understanding of continuous integration and continuous deployment practices, as well as experience with tooling like GitHub actions and Gitlab CIThese skills will help, but aren’t essential:Familiarity with cloudKnowledge of Infrastructure as Code (Terraform, Terragrunt)Tech Stack:ClickhouseDBTAirflowTerraform, Terragrunt, HelmAWS

SagemakerBedrock

Gitlab CIDVCMLFlow/Kubeflow/Weights & BiasesAbout us:FXC Intelligence is a leading provider of cross-border payments data and intelligence, providing some of the world's biggest companies, central banks and non-governmental organisations with the strategic insights, expertise and awareness to effectively compete in their chosen markets. By joining us, you will be diving into a world of data-driven exploration and innovation, revolutionising financial insights through cutting-edge technologies, machine learning and predictive analytics.Your contributions will shape the future of cross-border finance, helping clients to uncover better paths to growth and profitability, as well as being a trusted reference and source for many leading international publications.We are proud to produce industry-changing data and intelligence, aided by our company values of being customer-focused, taking ownership, knowledge, communication and leadership.We’re an innovative company that strives to look after its team and we take pride in providing a positive company culture. Have a look at our careers page to see for yourself what it’s like to work with us.Also, why not take a look at our employee engagement blog to see how our colleagues feel about working at FXC Intelligence!At FXC Intelligence, we believe in embracing diversity in all forms and fostering an inclusive environment. All applicants will be considered for employment without attention to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, veteran, neurodiversity status or disability status.

#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer - Computer Vision

Machine Learning Engineer (NLP)

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer, Computer Vision (Basé à London)

Machine Learning Engineer (12-month FTC) (Basé à London)

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Navigating Data Science Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Data science has taken centre stage in the modern workplace. Organisations rely on data-driven insights to shape everything from product innovation and customer experience to operational efficiency and strategic planning. As a result, there is a growing need for skilled data scientists who can analyse large volumes of data, build predictive models, communicate findings effectively, and collaborate cross-functionally. If you are looking to accelerate your data science career—or even land your first role—attending data science career fairs can be a game-changer. Unlike traditional online applications, face-to-face interactions let you showcase your personality, passion, and communication skills in addition to your technical expertise. However, to stand out in a busy environment, you need a clear strategy: from polishing your personal pitch and asking thoughtful questions to following up with a memorable message. In this article, we’ll guide you through every step of making a strong impression at data science career fairs in the UK and beyond.

Common Pitfalls Data Science Job Seekers Face and How to Avoid Them

Data science has become a linchpin for decision-making and innovation across countless industries, from finance and healthcare to tech and retail. The demand for data scientists in the UK continues to climb, with businesses seeking professionals who can interpret complex datasets, build predictive models, and communicate actionable insights. Despite this high demand, the job market can be extremely competitive—and many applicants unknowingly fall into avoidable traps. Whether you’re an aspiring data scientist fresh out of university, a professional transitioning from a quantitative role, or a seasoned analyst looking to expand your skill set, it’s crucial to navigate your job search effectively. In this article, we explore the most common pitfalls data science job seekers face and provide pragmatic advice to help you stand out. By refining your CV, portfolio, interview strategies, and communication skills, you can significantly increase your chances of landing a rewarding data science role. If you’re looking for your next data science job in the UK, don’t forget to explore the listings at Data Science Jobs. Read on to discover how to avoid critical mistakes and position yourself for success.

Career Paths in Data Science: From Entry-Level Analysis to Leadership and Beyond

Data is the lifeblood of modern business, and Data Scientists are the experts who turn raw information into strategic insights. From building recommendation engines to predicting market trends, the impact of data science extends across virtually every industry—finance, healthcare, retail, manufacturing, and beyond. In the UK, data-driven decision-making is critical to remaining competitive in a global market, making data science one of the most sought-after career paths. But how does one launch a career in data science, and how can professionals progress from entry-level analysts to senior leadership roles? In this comprehensive guide, we’ll explore the typical career trajectory, from junior data scientist to chief data officer, discussing the key skills, qualifications, and strategic moves you need to succeed. Whether you’re a recent graduate, transitioning from another technical field, or an experienced data scientist aiming for management, you’ll find actionable insights on forging a successful career in the UK data science sector.