Senior ML Ops Engineer (m/f/d) at Redcare Pharmacy

Cologne, North Rhine-Westphalia, Germany -

Full Time

Start Date

Immediate

Expiry Date

07 Sep, 26

Salary

98000.0

Posted On

09 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, DevOps, Cloud Platforms, Containerization, CI/CD Tools, ML Workflow Tools, Scripting Skills, Infrastructure-as-Code, Git, Analytical Skills, Problem-Solving Skills

Industry

Retail Pharmacies

Description

Company Description About Redcare Pharmacy: As Europe’s No.1 e-pharmacy, Redcare Pharmacy is powered by passionate teams and cutting-edge innovation. We strive to create a healthy, collaborative work environment where every employee feels valued and inspired to contribute to our vision, “Until every human has their health.” If you’re seeking a career that offers purpose and aligns with your values, join us and start your #Redcareer today. About the role: We are seeking a skilled MLOps Engineer (m/f/d) to support and optimize the deployment and monitoring of machine learning (ML) and large language model (LLM) solutions. In this role, you will bridge the gap between data science and engineering, ensuring scalable, efficient, and reliable deployment of models in production. You’ll work closely with data scientists, engineers, and product teams to design robust MLOps/LLMOps frameworks and contribute to a culture of innovation and best practices. Job Description About your tasks: Develop and maintain CI/CD pipelines for deploying ML and LLM models in production, ensuring rapid and reliable model iteration. Optimize the deployment process for LLMs, considering cost, latency, and scalability constraints. Automate model retraining and redeployment processes to respond to changing data patterns. Set up and manage cloud and on-premise infrastructure for ML/LLM workloads on platforms such as AWS, GCP, or Azure. Work with containerization and orchestration tools (e.g., Docker, Kubernetes) to ensure scalable and resilient model deployment. Implement data versioning and validation processes to maintain data integrity throughout the ML lifecycle. Use tools like MLflow for tracking experiments, managing model versions, and maintaining reproducibility. Document MLOps/LLMOps practices and mentor team members on deployment best practices. Qualifications About you: Experience in MLOps, DevOps, or a related role, with hands-on experience deploying ML/LLM models. Proficiency in cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Experience with CI/CD tools (e.g. Github Actions or Jenkins) and ML workflow tools (e.g., MLflow, Kubeflow). Strong scripting skills (Python, Bash) and familiarity with infrastructure-as-code tools (e.g., Terraform) and experience with Git or version control mechanisms. Strong analytical and problem-solving skills. Additional Information About your benefits: In order to provide you with the best possible support for your individual needs - whether it‘s living a healthy life, having enough time for your family, or developing your skills - we offer a wide range of different, site-specific benefits: Work from Home: If your job does not require you to be present in the office, we can arrange the place you work from individually - even for up to 20 days a year anywhere in the EU. Personal development: Grow! We support and encourage your individual development through various in- and external trainings. Redcare events: We promote teambuilding through creative team events, and celebrate our successes together at regularly scheduled parties. Corporate pension plan: We offer a company pension scheme and have a dedicated external consultant to offer you the best guidance and advice. Mental Health: Get quick and professional help from psychologists of nilo if you feel overwhelmed in private or professional life. Anonymous and free of charge. Compensation: EUR 78000 - EUR 98000 - yearly

Responsibilities

Develop and maintain CI/CD pipelines for deploying ML and LLM models in production. Optimize deployment processes and automate model retraining to ensure efficient operations.