At a Glance
- Tasks: Join the Accelerate AI team to develop innovative ML solutions for journalism.
- Company: Be part of the Financial Times, a leader in digital journalism and technology.
- Benefits: Enjoy competitive pay, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on diversity and career advancement.
- Why this job: Make a real impact by shaping the future of news with cutting-edge AI technology.
- Qualifications: Experience in ML algorithms, Python, and building scalable systems 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. 500+-people strong, 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, e.g. telling stories that we wouldn't have otherwise. 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
We Often Use These, It's Not An Exhaustive List But Gives You a Taste Of What Our Technology Stack And Tools Look Like:
- 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. Being able to establish and be a promoter of 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. Supporting 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.
Desirable:
- 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 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 growing Accelerate AI team, MLOps Engineers will have the unique opportunity to work with cutting-edge technologies in a dynamic environment, while benefiting from continuous professional development and a commitment to diversity. Join us in shaping the future of journalism through advanced AI solutions, all within a supportive and forward-thinking team atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land MLOps Engineer
✨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! Prepare a portfolio or a GitHub repository showcasing your projects related to machine learning and NLP. This will not only demonstrate your expertise but also give you something tangible to discuss during interviews.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by brushing up on your coding skills, especially in Python and Java. Use platforms like LeetCode or HackerRank to solve problems that are relevant to the role.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Financial Times team. Don’t forget to tailor your application to highlight your experience with ML models and cloud platforms!
We think you need these skills to ace MLOps Engineer
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 algorithms, containerization, and any relevant programming languages like Python and Java. 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 your background makes you a great fit for our team. Don’t forget to mention your experience with AI and data science – we love that stuff!
Showcase Your Projects:If you've worked on any relevant projects, make sure to include them in your application. Whether it's a personal project or something from a previous job, we want to see your hands-on experience with ML pipelines and APIs. It helps us understand your practical skills!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy and ensures your application goes directly to us. Plus, you’ll get to see more about our culture and what we’re all about!
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, AWS, and Kubernetes. Be ready to discuss your experience with these tools and how you've used them in past projects.
✨Showcase Your MLOps Experience
Prepare specific examples of how you've productionised machine learning algorithms or data science models. Highlight any experience you have with containerisation and scaling, as this will be crucial for the role.
✨Collaborative Mindset
The role involves working closely with Data Scientists and Product Managers. Be prepared to discuss how you've successfully collaborated in the past, and share examples of how you’ve contributed to team projects.
✨Understand the Business Context
Research the Financial Times and its mission. Think about how your skills can contribute to their goals, especially in developing generative AI products. This shows that you're not just technically skilled but also aligned with their vision.
