MLOps Engineer

The MLOps Engineer at Kalibri Labs is tasked with developing a secure continuous deployment pipeline, working with cross-functional teams to enhance data science solutions, integrating aspects of a secure SDLC, and implementing CI/CD processes that support the machine learning lifecycle. You will also automate deployment processes, ensuring that they are scalable and efficient, and keep an eye on monitoring tools to preemptively address any issues that may arise.

To qualify for the MLOps Engineer role at Kalibri Labs, candidates should have a Bachelor's Degree in Computer Science, Information Systems, or a related field, alongside at least 3 years of experience in managing deployment pipelines with data science packages like TensorFlow and PyTorch. Additionally, familiarity with AWS for cloud services and tools like Terraform for automation is essential.

As an MLOps Engineer at Kalibri Labs, your role is crucial in redefining the way performance metrics are viewed in the hotel industry. By building reliable and efficient data pipelines and ensuring seamless deployment of machine learning models, you'll help provide actionable insights for our clients to navigate their recovery processes effectively.

The MLOps Engineer at Kalibri Labs utilizes a variety of tools and technologies, including AWS for cloud services, Docker for containerization, and automation tools like Terraform and Ansible. Experience with observability tools like Grafana, New Relic, and monitoring systems like Prometheus is also vital, as it directly impacts system performance and reliability.

MLOps Engineers at Kalibri Labs enjoy a fully remote work environment with a range of benefits, including medical, dental, and vision insurance coverage at 75%, a 50% matching for 401k contributions, life insurance, long-term disability coverage, and a home office setup allowance. These benefits underscore our commitment to the well-being of our employees.

When responding, be specific about your former roles involving CI/CD, including the tools you have used, such as GitHub Actions or Airflow, and how those experiences have shaped your approach to automation in machine learning lifecycles.

Explain your methodology for establishing a secure deployment pipeline, addressing aspects such as integrating security best practices throughout the SDLC and methods to ensure data privacy and compliance.

Provide concrete examples where your automation efforts led to improved efficiency, detailing the tasks automated, the tools used, and the measurable outcomes that resulted.

Discuss a specific instance where you faced technical limitations in an MLOps environment, your analysis of the issue, the steps you took to solve it, and the positive impact of your solution.

Highlight your experience with specific AWS services, your role in managing cloud resources, and how you have optimized costs while ensuring reliability and security in past projects.

Discuss the importance of observability in maintaining application performance, mentioning the tools you use such as Grafana or Datadog, to monitor system health and proactively address potential issues.

Illustrate your experience in cross-functional team settings, emphasizing your communication skills and how you have effectively collaborated with data scientists, software engineers, and product managers to drive project success.

Provide a clear and concise explanation of hyperparameter tuning, detailing your approach and any tools or frameworks you have employed to enhance model accuracy and performance.

Share what specifically excites you about Kalibri Labs—be it their mission in the hospitality industry, the innovative use of AI and machine learning, or their commitment to employee development and remote work options. This will reflect your enthusiasm and alignment with their values.

Discuss the resources you utilize to stay informed, such as webinars, online communities, news platforms, or educational courses focusing on MLOps trends and best practices.