Senior MLOps Engineer

The Job:

As an ML Ops Engineer, you will bridge the gap between our data science and engineering teams, ensuring the seamless deployment and operation of our machine learning models. You will be responsible for designing, building, and maintaining the infrastructure and pipelines that support our machine learning applications. Your primary goal will be to ensure the reliability, scalability, and performance of our machine learning systems.

Key Responsibilities:

• Model Deployment: Collaborate with data scientists to deploy machine learning models into production, ensuring smooth integration with existing infrastructure.
• Model Serving: Design and implement model serving architectures that can handle large volumes of requests and provide low-latency responses.
• Monitoring and Logging: Develop monitoring and logging systems to track model performance, identify issues, and enable data-driven decision-making.
• Model Maintenance: Develop and implement processes for model updates, retraining, and redeployment, ensuring minimal downtime and optimal performance.
• Infrastructure Management: Manage and maintain the infrastructure supporting our machine learning applications, including cloud resources, containerization, and orchestration tools.
• Collaboration: Work closely with data scientists, engineers, and product managers to identify and prioritize project requirements, develop solutions, and ensure successful project delivery.
• Security and Compliance: Ensure the security and compliance of our machine learning systems, adhering to industry standards and regulatory requirements.

Requirements:

• Education: Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field.
• 3+ years of experience in machine learning operations, software engineering, or a related field.
• Technical Skills:
  • Proficiency in Python, Go, C++
  • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Familiarity with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes).
  • Knowledge of cloud platforms (e.g., AWS, GCP, Azure).
  • Experience with monitoring and logging tools (e.g., Prometheus, Grafana).

• Soft Skills:
  • Excellent communication and collaboration skills.
  • Strong problem-solving and analytical skills.
  • Ability to work in a fast-paced environment and prioritize multiple tasks.

Nice to Have:

1. Experience with MLOps tools: Familiarity with MLOps platforms and tools, such as MLflow, TensorFlow Extended, or AWS SageMaker.
2. Knowledge of DevOps practices: Experience with continuous integration, continuous deployment, and continuous monitoring.

• Competitive salary package and and attractive project token bonus
• Flexible working time and open, dynamic environment with stunning colleagues
• Healthy lunches and free flow of fresh milk, coffee, tea, and more while working in our fabulous office
• Sick leave, holidays, and at least 12 paid days off annually.
• Full medical and dental coverage for you.
• MacBook Pro, monitor, smart desk & chais and any accessories provided to do your best work
• Free access to a wide variety of online courses