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- Tasks: Manage and optimise the machine learning lifecycle, from development to deployment.
- Company: Join TEKEVER, a leader in unmanned technology and maritime safety.
- Benefits: Flexible work arrangements, professional development, and a collaborative environment.
- Why this job: Make a real impact on global safety with cutting-edge AI technologies.
- Qualifications: 3+ years in machine learning or DevOps, with strong programming skills.
- Other info: Dynamic team culture with opportunities for innovation and growth.
The predicted salary is between 36000 - 60000 £ per year.
Are you ready to revolutionise the world with TEKEVER?
Join us, the European leader in unmanned technology, where cutting-edge advancements meet unparalleled innovation. We offer a unique surveillance-as-a-service solution that provides real-time intelligence, enhancing maritime safety and saving lives. TEKEVER is setting new standards in intelligence services, data and AI technologies.
Become part of a dynamic team transforming maritime surveillance and making a significant impact on global safety.
At TEKEVER, our mission is to provide limitless support through mission-oriented game-changers, delivering the right information at the right time to facilitate critical decisions.
If you’re passionate about technology and eager to shape the future, TEKEVER is the place for you!
Job Overview:
As an MLOps Engineer, you will be responsible for managing and optimizing the machine learning lifecycle, from model development to deployment and monitoring. You will work closely with data scientists, software engineers and IT operations teams to ensure seamless integration, scalability and reliability of machine learning models in production environments. The ideal candidate will have a strong background in both machine learning and DevOps, with experience in building and maintaining robust MLOps pipelines.
What will be your responsibilities:
- Pipeline Development: Design, implement and maintain scalable and efficient machine learning pipelines that automate the process of model training, testing, deployment and monitoring.
- Model Deployment: Collaborate with data scientists to deploy machine learning models to production environments, ensuring they are scalable, reliable and secure.
- CI/CD Integration: Develop and maintain continuous integration and continuous deployment (CI/CD) processes for machine learning models, ensuring seamless updates and version control.
- Infrastructure Management: Set up and manage cloud-based and on-premise infrastructure for machine learning workflows, including data storage, computing resources and model serving platforms.
- Monitoring and Maintenance: Monitor the performance and health of deployed models, implementing automated systems for anomaly detection, logging and alerting to ensure high availability and performance.
- Collaboration: Work closely with cross-functional teams, including data scientists, software developers and IT operations, to define requirements and deliver solutions that meet business and technical needs.
- Security: Implement best practices for data security, model governance and compliance, ensuring that machine learning workflows adhere to industry standards and regulations.
- Documentation: Maintain comprehensive documentation of MLOps processes, infrastructure and best practices for future reference and reproducibility.
- Innovation: Stay current with the latest advancements in MLOps tools and technologies, continuously improving and evolving the MLOps processes and infrastructure.
Profile and requirements:
- Education: Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field
- Experience: 3+ years of experience in machine learning, DevOps, or a related field, with specific experience in MLOps.
- Technical Skills:
- Proficiency in programming languages such as Python, Go, Rust, or a similar language.
- Experience with machine learning and deep learning frameworks such as TensorFlow, TensorRT, PyTorch, or scikit-learn.
- Strong knowledge of DevOps practices, including CI/CD, infrastructure as code (IaC) and containerization (Docker, Kubernetes).
- Experience with version control systems (e.g., Git) and collaborative development tools.
- Understanding of data engineering concepts and tools for data preprocessing and ETL.
- Knowledge of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
- Experience with relevant tooling such as ClearML for ML lifecycle management.
- Experience in getting machine learning products to production.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, with focus on Google Cloud.
- Analytical Skills: Excellent analytical and problem-solving skills with the ability to design innovative solutions to complex problems.
- Communication: Strong verbal and written communication skills, with the ability to effectively collaborate with technical and non-technical stakeholders.
- Attention to Detail: High attention to detail and a commitment to ensuring the accuracy and quality of work.
- Adaptability: Ability to thrive in a fast-paced, dynamic environment and manage multiple projects simultaneously.
What we have to offer you:
- An excellent work environment and an opportunity to create a real impact in the world;
- A truly high-tech, state-of-the-art engineering company with flat structure and no politics;
- Working with the very latest technologies in Data & AI, including Edge AI, Swarming – both within our software platforms and within our embedded on-board systems;
- Flexible work arrangements;
- Professional development opportunities;
- Collaborative and inclusive work environment;
- Salary compatible with the level of proven experience.
MLOps Engineer employer: TEKEVER
Contact Detail:
TEKEVER Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your MLOps projects, including any pipelines you've built or models you've deployed. This gives potential employers a tangible look at what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps questions and scenarios. Practice explaining your past experiences and how they relate to the role you're applying for. Confidence is key!
✨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 our team at TEKEVER.
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, DevOps, and any relevant tools or technologies mentioned in the job description. 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 technology and how you can contribute to TEKEVER's mission. Be genuine and let your personality come through – we love to see enthusiasm!
Showcase Your Projects: If you've worked on any relevant projects, whether personal or professional, make sure to mention them. We’re interested in seeing how you've applied your skills in real-world scenarios, especially in building MLOps pipelines or deploying models.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us that you’re genuinely interested in joining our team at TEKEVER!
How to prepare for a job interview at TEKEVER
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical skills listed in the job description. Brush up on your knowledge of machine learning frameworks like TensorFlow and PyTorch, as well as DevOps practices such as CI/CD and containerisation. Being able to discuss these topics confidently will show that you’re ready to hit the ground running.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled complex problems in MLOps or related fields. Think about challenges you've faced in model deployment or pipeline development and how you overcame them. This will demonstrate your analytical skills and ability to innovate under pressure.
✨Collaboration is Key
Since the role involves working closely with data scientists and software engineers, be ready to talk about your experience in cross-functional teams. Highlight instances where you’ve successfully collaborated to deliver solutions, as this will show that you can communicate effectively with both technical and non-technical stakeholders.
✨Stay Current with Industry Trends
Research the latest advancements in MLOps tools and technologies before your interview. Being able to discuss recent trends or innovations will not only impress your interviewers but also show your passion for the field and commitment to continuous improvement.
