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- Tasks: Join us as a Senior MLOps Engineer, optimising ML model deployments and collaborating with data scientists.
- Company: Optimove is a leading marketing tech company, working with top brands like Sephora and Staples.
- Benefits: Enjoy a vibrant startup culture, career growth opportunities, and the chance to impact cutting-edge technology.
- Why this job: Be part of a dynamic team where your contributions directly influence ML initiatives and operational excellence.
- Qualifications: 4+ years in MLOps or software engineering, strong Python skills, and AWS experience required.
- Other info: Work in a collaborative environment with a focus on knowledge sharing and innovation.
The predicted salary is between 48000 - 84000 £ per year.
Optimove is a global marketing tech company, recognised as a Leader by Forrester and a Challenger by Gartner. We work with some of the world's most exciting brands, such as Sephora, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fast-growing startup, we're on the cusp of our next growth spurt. It's the perfect time to join our team of ~500 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed.
Based in Dundee, Scotland, our R&D operation is a dynamic environment, where every developer can impact the flow of technology – from introducing the smallest library to making big infrastructure changes. We welcome open-minded developers who like to share knowledge and help each other to push Optimove forward using the cutting edge of today’s tech.
The new MLOps team will be responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production. Acting as the critical link between the data science and R&D teams, this team will ensure that ML models transition smoothly from development to production, maintaining high availability, scalability, and performance.
Key responsibilities include:
- 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.
This team will play a pivotal role in ensuring that ML initiatives drive value effectively while maintaining operational excellence.
Responsibilities:
- Architect and develop robust pipelines for ML model training, testing, and deployment.
- Implement and maintain CI/CD workflows for ML projects.
- Monitor production ML systems for performance, errors, and drift.
- Automate infrastructure provisioning and deployment using IaC tools.
- Collaborate with team leader to define technical strategies.
Requirements:
- 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.
- Strong communication and collaboration skills.
Senior MLOps Engineer employer: Optimove
Contact Detail:
Optimove Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLOps Engineer
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as AWS services and CI/CD pipelines. Having hands-on experience or projects that showcase your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with current employees or professionals in the MLOps field. Engaging with them on platforms like LinkedIn can provide insights into the company culture and expectations, which can be invaluable during interviews.
✨Tip Number 3
Prepare to discuss real-world scenarios where you've successfully deployed ML models or optimised existing systems. Being able to articulate your problem-solving process and the impact of your work will demonstrate your expertise.
✨Tip Number 4
Stay updated on the latest trends and advancements in MLOps and machine learning. Showing that you're proactive about learning and adapting to new technologies can impress hiring managers and show your commitment to the field.
We think you need these skills to ace Senior MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, DevOps, or software engineering. Emphasise your programming skills in Python and any familiarity with ML frameworks, as well as your experience with AWS services.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for machine learning and your understanding of the role. Mention specific projects where you've implemented CI/CD workflows or automated infrastructure provisioning, and how these experiences align with Optimove's needs.
Showcase Collaboration Skills: In your application, highlight instances where you've collaborated with data scientists or R&D teams. This is crucial for the role, so provide examples of how you’ve worked together to implement model requirements or align technical strategies.
Demonstrate Problem-Solving Abilities: Include examples of how you've troubleshot complex production systems in your previous roles. This will show your capability to handle challenges that may arise in the deployment and monitoring of ML models.
How to prepare for a job interview at Optimove
✨Showcase Your MLOps Expertise
Make sure to highlight your experience in MLOps, DevOps, or software engineering roles. Be prepared to discuss specific projects where you've implemented CI/CD workflows or managed ML model deployments, as this will demonstrate your hands-on knowledge and problem-solving skills.
✨Familiarise Yourself with AWS Services
Since the role requires extensive experience with AWS services like SageMaker and ECS, brush up on these tools before the interview. Be ready to explain how you've used them in past projects and how they can be leveraged for Optimove's needs.
✨Prepare for Technical Questions
Expect technical questions related to Python programming, containerization, and orchestration tools like Docker and Kubernetes. Practising coding problems or discussing your approach to troubleshooting complex production systems can help you feel more confident.
✨Emphasise Collaboration Skills
As the role involves working closely with data scientists and R&D teams, be sure to highlight your communication and collaboration skills. Share examples of how you've successfully partnered with others to achieve project goals, as this will show that you're a team player who can thrive in Optimove's dynamic environment.
