At a Glance
- Tasks: Join the Accelerate AI team to develop and implement innovative generative AI products.
- Company: Be part of the Financial Times, a leader in gold-standard journalism and tech innovation.
- Benefits: Enjoy competitive salary, health benefits, and opportunities for remote work and professional growth.
- Other info: Dynamic team environment with excellent career advancement opportunities.
- Why this job: Make a real impact by working on cutting-edge AI solutions that enhance storytelling.
- Qualifications: Experience in machine learning, Python, and developing scalable solutions is essential.
The predicted salary is between 60000 - 80000 £ per year.
About Us
Here at the Financial Times, gold-standard journalism is just the beginning. Our Product & Tech team keeps us ahead of the ever-changing digital landscape by delivering cutting-edge products to over one million digital subscribers every day. Our plans for growth rely on a diverse, dedicated and dynamic group of product, tech, delivery and data specialists - everyone’s welcome in this friendly, forward-thinking team.
Role & Team Overview
The Accelerate AI team is being formed. The team pulls together the diverse knowledge and skills from our product, tech and data teams to move fast in both experimenting and developing full end-to-end products. This team is dedicated to speeding up the development and implementation of generative AI products at FT. The first challenge the team is taking on is enabling the newsroom to create more value through summarisation and story finding. The scope will also include developing strategies for new channels and consumption behaviours, and audience-facing products.
The MLOps engineer role will also be part of a 4-strong MLOps team that are growing.
Our Tech Stack
- Python
- R
- SQL
- Knowledge graph
- SingleStore
- BigQuery
- Machine Learning platforms like Posit/RStudio
- GitHub
- CircleCI
- AWS: ECS/EKS, CloudFormation, Redshift
- Kubernetes: Helm charts, kubectl, eksctl
- Streaming technologies like Kafka, Spark, or Flink
- GPU technologies
- Splunk
- Prometheus
- Graphite
- Grafana
What You’ll Be Doing
- Champion, install, and develop frameworks for software engineering best practices within NLP and Machine Learning for content use cases.
- Support the GenAI Accelerate team and the Data Scientist(s) in that team with building, documenting and testing machine learning pipelines in line with FT Data Science Team process and best practice.
- Work collaboratively with Data Scientists, Data engineers and Product managers to deploy and operate systems.
- Design and implement low maintenance, well monitored, secure and scalable solutions to the problems the GenAI Accelerate team is solving.
- Establish and promote good coding and engineering practices for NLP and ML within GenAI Accelerate team.
- Contribute to company-wide processes, frameworks and guidelines.
- Develop an in-depth understanding of FT’s underlying data and data flow, data structures.
- Support engineering product support on new capabilities and enhancements, such as custom Search APIs.
What we are looking for
Essential- Experience productionizing Machine learning algorithms or Data science models.
- Experience with containerization, scaling and load balancing, specifically for ML models.
- Experience designing and developing RESTful APIs.
- Highly proficient in the programming languages relevant to the Data and Content Analytics domain at the FT - Python and Java.
- Experience in developing end-to-end (Data/Dev/ML)Ops pipelines based on in-depth understanding of cloud platforms, AI lifecycle, and business problems to ensure analytics solutions are delivered efficiently, predictably, and sustainably.
- Experience with streaming applications such as Kafka streams, Spark streaming.
- Experience in modern database technologies (AWS/cloud-based/in-memory etc.), scripting languages, big data technologies.
- Experience with Kubernetes.
- Experience developing monitoring and maintenance systems for ML models, specifically those based on text data using NLP technologies.
- Experience developing cutting edge search capabilities using the latest technologies in semantic search.
- Good understanding of the principles and trade-offs of a microservices architecture.
- Experience in working with ETL frameworks (job orchestration tools) such as Airflow or Luigi.
MLOps Engineer in London employer: Financial Times
At the Financial Times, we pride ourselves on being an exceptional employer, fostering a collaborative and innovative work culture that empowers our employees to thrive. As part of the dynamic Product & Tech team, MLOps Engineers will have access to cutting-edge technology, opportunities for professional growth, and the chance to contribute to impactful projects in generative AI. With a commitment to diversity and inclusion, we ensure that every voice is heard, making FT not just a workplace, but a community where your career can flourish.
StudySmarter Expert Advice🤫
We think this is how you could land MLOps Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at the Financial Times. LinkedIn is your best mate here; drop them a message, ask about their experiences, and see if they can give you the inside scoop on the MLOps Engineer role.
✨Tip Number 2
Show off your skills! If you've got a portfolio or GitHub with projects related to machine learning or NLP, make sure to highlight that. It’s a great way to demonstrate your hands-on experience and passion for the tech stack they use.
✨Tip Number 3
Prepare for the interview by brushing up on your knowledge of the tools mentioned in the job description. Get comfy with Python, AWS, and Kubernetes, and be ready to discuss how you've used them in past projects. We want to see your problem-solving skills in action!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the Financial Times team. Let’s get you that MLOps Engineer role!
We think you need these skills to ace MLOps Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the MLOps Engineer role. Highlight your experience with machine learning, containerization, and any relevant tech stack you've worked with. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how you can contribute to our GenAI Accelerate team. Be genuine and let your personality come through – we love that!
Showcase Your Projects:If you've worked on any cool projects related to MLOps or machine learning, make sure to mention them! Whether it's a personal project or something from a previous job, we want to see your hands-on experience and creativity.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company and culture!
How to prepare for a job interview at Financial Times
✨Know Your Tech Stack
Familiarise yourself with the technologies mentioned in the job description, like Python, SQL, and AWS. Be ready to discuss how you've used these tools in past projects, especially in relation to MLOps and machine learning.
✨Showcase Your Collaboration Skills
The role involves working closely with Data Scientists and Product Managers. Prepare examples of how you've successfully collaborated in a team setting, particularly in developing and deploying ML models or APIs.
✨Demonstrate Problem-Solving Abilities
Think of specific challenges you've faced in previous roles related to MLOps or data engineering. Be ready to explain your thought process and the steps you took to overcome these challenges, especially in terms of scalability and efficiency.
✨Ask Insightful Questions
Prepare thoughtful questions about the GenAI Accelerate team's goals and the company's vision for AI products. This shows your genuine interest in the role and helps you understand how you can contribute to their success.
