At a Glance
- Tasks: Build and manage cutting-edge machine learning solutions in production.
- Company: Join a forward-thinking tech company focused on innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting projects and career advancement potential.
- Why this job: Make an impact by deploying scalable ML models and collaborating with top talent.
- Qualifications: 5-8 years in MLOps or Machine Learning Engineering with strong cloud expertise.
The predicted salary is between 63000 - 77000 £ per year.
We are looking for an experienced MLOps Engineer (L3/L4) with 5-8 years of experience to build, deploy, monitor, and manage machine learning solutions in production.
The ideal candidate should have strong expertise in cloud platforms, CI/CD, containerization, automation, and machine learning lifecycle management.
Key Responsibilities
- Design, build, and maintain scalable MLOps pipelines.
- Deploy and manage machine learning models in production.
- Develop CI/CD pipelines for ML workflows.
- Automate model training, testing, deployment, and monitoring.
- Work closely with Data Scientists, Data Engineers, and Software Engineers.
- Monitor model performance and ensure reliability and scalability.
- Manage infrastructure using Infrastructure as Code (Ia C).
- Implement security, governance, and best practices for ML platforms.
- Troubleshoot production issues and optimize ML systems.
- Required Skills
- 5-8 years of experience in MLOps, Dev Ops, or Machine Learning Engineering.
- Strong experience with Azure Machine Learning, Azure Dev Ops, or Databricks.
- Good knowledge of Python and SQL.
- Experience with Docker and Kubernetes.
- Hands-on experience with CI/CD tools such as Azure Dev Ops, Git Hub Actions, or Jenkins.
- Knowledge of ML lifecycle management and model deployment.
- Experience with Git version control.
- Familiarity with Terraform or other Infrastructure as Code tools.
- Understanding of monitoring tools such as Prometheus, Grafana, or Azure Monitor.
- Preferred Skills
- Experience with MLflow.
- Knowledge of Apache Airflow or similar workflow orchestration tools.
- Experience with Spark and Databricks.
- Knowledge of REST APIs and microservices.
- Understanding of cloud security and governance.
- Experience with Generative AI or Large Language Model (LLM) deployment is an added advantage.
Qualifications
- Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, or a related field.
- Relevant Azure, Kubernetes, or Databricks certifications are preferred.
- Good to Have
- Strong communication and problem-solving skills.
- Experience working in Agile/Scrum environments.
- Ability to collaborate with cross-functional teams.
- Experience supporting enterprise-scale machine learning platforms.
- #J-18808-Ljbffr
Machine Learning Engineer in London employer: Quantum World Technologies Inc.
Quantum World Technologies Inc. is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for professionals in AI Engineering and MLOps. With a commitment to employee growth, we offer extensive training opportunities and a supportive environment that encourages creativity and leadership. Located in a vibrant tech hub, our team enjoys access to cutting-edge resources and a network of industry experts, ensuring a rewarding and impactful career path.
Contact Details:
Quantum World Technologies Inc. Recruitment Team
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