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- Tasks: Develop and maintain cloud infrastructure for AI-driven tools in biotechnology.
- Company: Join an innovative biotech startup focused on AI and live cell imaging.
- Benefits: Enjoy competitive salary, growth opportunities, and a collaborative remote work environment.
- Why this job: Make a real impact in healthcare by advancing scientific discovery and therapeutic development.
- Qualifications: 5+ years in DevOps/MLOps with strong skills in GCP, Kubernetes, and Python.
- Other info: Be part of a rapidly growing team tackling complex challenges at the intersection of AI and biology.
The predicted salary is between 43200 - 72000 £ per year.
About Us
We are an early-stage biotechnology company using live cell imaging and artificial intelligence to predict stem cell behavior. Our goal is to improve the manufacturing of human cells for research and therapeutic use by enabling a deeper understanding of cell differentiation. Founded in 2022 as a spin-out from a leading UK research institution, we are backed by prominent venture capital firms with experience investing in AI, life sciences, and deep tech startups.
What We’re Building
At the core of our technology is a proprietary AI-powered SaaS platform that enables scientists to visualize, track, and predict cell differentiation in real-time. Designed to support high-throughput experiments, the platform integrates microscopy, computer vision, cloud infrastructure, and machine learning to accelerate advancements in cell biology and biomanufacturing.
We are currently seeking a DevOps / MLOps Engineer to help scale our cloud infrastructure and machine learning workflows as part of our growing technical team.
Key Responsibilities
- Develop and maintain infrastructure for our SaaS platform that delivers AI-driven computer vision tools to researchers and scientists.
- Collaborate with a multidisciplinary team of machine learning engineers, data scientists, software developers, and biologists.
- Build and support GPU-accelerated environments for training and real-time inference of deep learning models.
- Deploy and manage ML pipelines using tools like Docker, Kubernetes, and frameworks such as Kubeflow or Ray.
- Create and document APIs that enable internal and external users to access data and model outputs.
- Implement secure authentication and authorization systems for platform users.
- Maintain and improve our cloud platform’s reliability, security, and compliance (e.g., GDPR, HIPAA readiness).
- Automate testing, training, and deployment of models through robust CI/CD pipelines.
- Monitor and troubleshoot performance issues across data and inference workflows in production.
What We’re Looking For
- 5+ years of experience in DevOps, MLOps, SRE, or Data Engineering roles.
- Strong proficiency with public cloud platforms (e.g., GCP, AWS, or Azure), with preference for GCP.
- Expertise in Terraform and infrastructure-as-code practices.
- Solid experience deploying workloads with Kubernetes, including cluster and node management.
- Familiarity with ML workload orchestration using Docker, Kubeflow, Ray, or similar tools.
- Skilled in Python and comfortable working with SQL and data processing tools.
- Understanding of the machine learning lifecycle from data ingestion to inference.
- Experience handling large-scale datasets and optimizing data pipelines.
- Strong communication skills and the ability to clearly document complex systems.
- A self-driven mindset and interest in staying current with trends in cloud, data, and ML tools.
- Experience leading infrastructure efforts in greenfield or early-stage environments.
Nice to Have
- Experience working on SaaS platforms in biotech, healthcare, or life sciences.
- Experience with real-time ML inference and production monitoring.
- Familiarity with computer vision models and workflows.
- Understanding of data privacy regulations and scientific data formats (e.g., TIFF, OME-TIFF).
- Background working in early-stage startups (seed or Series A).
What We Offer
- Competitive salary and benefits.
- Growth opportunities and professional development.
- A collaborative, forward-thinking environment at the intersection of AI and biotechnology.
- A meaningful mission with the potential to impact healthcare through innovation in science and technology.
Why Join Us
You’ll be part of a rapidly growing, interdisciplinary team working on complex challenges in AI and biology. Your contributions will shape the technical foundation of our product and accelerate both scientific discovery and therapeutic development. This is an opportunity to make a tangible impact during a pivotal stage of growth.
MLOps Engineer | Python | Machine Learning | GCP | Kubernetes | CI/CD | Remote, UK employer: Enigma
Contact Detail:
Enigma Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer | Python | Machine Learning | GCP | Kubernetes | CI/CD | Remote, UK
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as GCP, Kubernetes, and CI/CD practices. Having hands-on experience or projects showcasing your skills with these tools can significantly boost your chances.
✨Tip Number 2
Network with professionals in the biotech and AI fields. Attend relevant meetups or webinars to connect with people who work in similar roles. This can provide insights into the company culture and potentially lead to referrals.
✨Tip Number 3
Stay updated on the latest trends in MLOps and cloud technologies. Follow industry leaders on social media and read up on recent advancements. This knowledge can help you during interviews when discussing how you can contribute to the company's goals.
✨Tip Number 4
Prepare to discuss your previous experiences in scaling cloud infrastructure and managing ML workflows. Be ready to share specific examples of challenges you've faced and how you overcame them, as this will demonstrate your problem-solving abilities.
We think you need these skills to ace MLOps Engineer | Python | Machine Learning | GCP | Kubernetes | CI/CD | Remote, UK
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, DevOps, and cloud platforms like GCP. Emphasise your skills in Python, Kubernetes, and CI/CD practices, as these are crucial for the role.
Craft a Compelling Cover Letter: Write a cover letter that connects your background to the company's mission in biotechnology and AI. Mention specific projects or experiences that demonstrate your ability to develop and maintain cloud infrastructure and ML workflows.
Showcase Relevant Projects: If you have worked on any projects involving machine learning pipelines, cloud infrastructure, or real-time inference, be sure to include these in your application. Detail your role and the technologies used to give a clear picture of your capabilities.
Highlight Soft Skills: In addition to technical skills, emphasise your communication abilities and teamwork experience. The role requires collaboration with multidisciplinary teams, so showcasing your interpersonal skills can set you apart.
How to prepare for a job interview at Enigma
✨Showcase Your Technical Skills
Be prepared to discuss your experience with GCP, Kubernetes, and CI/CD pipelines in detail. Highlight specific projects where you've successfully implemented these technologies, as this will demonstrate your hands-on expertise.
✨Understand the Company’s Mission
Familiarise yourself with the company's focus on biotechnology and AI. Be ready to explain how your skills can contribute to their mission of improving cell manufacturing and research, showing that you align with their goals.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities, especially related to machine learning workflows and data pipeline optimisation. Practice articulating your thought process clearly and logically.
✨Demonstrate Collaboration Skills
Since the role involves working with a multidisciplinary team, be ready to share examples of how you've effectively collaborated with others in past roles. Emphasise your communication skills and ability to document complex systems.
