We're a team of physicians, healthcare executives, data scientists and Tech experts committed to empowering anyone, anywhere with the insights they need to make well-informed, healthcare decisions. Through the combined power of AI and big data, we have created the first all-in-one solution that provides our users with everything they need to know about medical professionals and facilities. RYTE transforms the comprehensive data we collect on millions of healthcare providers and medical experts worldwide into knowledge that helps individuals and organizations navigate healthcare systems. Headquartered in Toronto (Canada) and having operations in Canada, France, Kazakhstan, and Philippines, you will join a truly international, multi-cultural, and dynamic workforce driven toward building something unique and that affects Life and Healthcare on a global scale. For more information about Us, please connect with us at www.ryte.ai.
We are looking for a MLOps Engineer who is passionate about technologies and automation, ready to roll up your sleeves to tackle complex technological challenges. Join our team as a MLOps Engineer and play a pivotal role in designing, implementing, and optimizing robust machine learning pipelines, facilitating the seamless transition of data models into production while ensuring reliable and scalable performance. This is a full-time position, working at the Astana office in Kazakhstan. No remote work for this position. You will be working full time for the Ryte team under the supervision of the Ryte team lead.
This role involves active participation in a dynamic and fast-paced IT environment. Leveraging your technical proficiency, you'll contribute to the architectural design and technical implementation of the MLOps framework for various projects, aligning with the organization's strategies focused on SaaS, big data analytics, and artificial intelligence. Your responsibilities encompass the end-to-end process of designing, developing, testing, deploying, and enhancing continuous integration (CI), continuous delivery (CD), and continuous training (CT) pipelines tailored for machine learning (ML) systems.
- Job Titled: MLOps Engineer
- Job Type: Full-time position
- Language: English
- Work Location: Astana office in Kazakhstan
- Develop and establish the MLOps framework and pipelines, encompassing integration, testing, releasing, deployment, and governance of machine learning models.
- Create and implement operational systems and tests to ensure the security, performance, and availability of ML models.
- Oversee the management of continuous integration (CI), continuous delivery (CD), and continuous training (CT) pipelines.
- Architect and build reusable components and operational strategies on the Azure Cloud, with a focus on scalability, high availability, performance, monitoring, and observability.
- Integrate the MLOps framework into the organization's strategy, aligning traditional CI/CD practices with modern ML requirements.
- Explore and prototype novel approaches to address business challenges using new technologies, design patterns, and development models.
- Serve as a subject matter expert in MLOps, establishing and maintaining enterprise standards, user guides, release notes, and FAQs.
- Establish processes that facilitate seamless MLOps operations, including application monitoring, troubleshooting, lifecycle management, and customer support.
- Effectively communicate solutions and collaborate with stakeholders and solution partners, while also reviewing product changes and development needs.
- Cultivate strong relationships with the application user base, creating educational and communication content in alignment with lifecycle events.
- Stay at the forefront of emerging tools and technologies through active research, demonstrating a willingness to learn and experiment.
Bachelor’s degree or higher required, preferably in the fields of computer science, IT, MIS, or engineering
Hands-on coding skills Python 3 (e.g.,Understanding Data Structure and algorithms, API including automated testing frameworks and libraries (e.g., pytest), infrastructure as code (e.g., TerraForm), and Kubernetes artifacts (e.g., deployments, operators, helm charts), als knowledge about pyspark is preferable.
Knowledge about Data science project life cycle and hands-on experience.
Experience in building and design (architecture, design patterns, reliability, and scaling) of production-grade Cloud and DevOps applications, preferably solving for multiple teams and analytics use cases
On-the-job experience working with data teams and automating ML and other data-intensive applications development workflows.
Expertise delivering solutions through others and leading teams through problem solving on deep technical issues
Excellent hands-on, expert knowledge of cloud platform infrastructure and administration (Azure / AWS / GCP) with strong knowledge of cloud services integration, and cloud security. Preferably Azure.
Experience architecting complete cloud-based solutions and working with development teams on delivery
Expertise setting up CI/CD processes, building and maintaining secure DevOps pipelines with at least 1 major DevOps stacks (e.g., Azure DevOps, ML-flow, Argo)
Experience with modern development methods and tooling containers (e.g., Docker) and container orchestration (K8s), CI/CD tools (e.g., Circle CI, Jenkins, GitHub actions, Azure Devops), version control (Git, Github, Gitlab), orchestration / DAGs tools (e.g., Argo, Airflow, Kubeflow)
Experience setting up at least one contemporary MLOps tooling (e.g., experiment tracking, model governance, packaging, deployment, feature store)
Practical knowledge delivering and maintaining production software such as APIs and cloud infrastructure
Knowledge of SQL (intermediate level or higher is preferred) and familiarity working with at least one common RDBMS, such as mySQL, Postgres, SQL Server, or Oracle
We would like to thank all applicants for their interest in this position. Please note that only applicants selected for an interview will be contacted. Ryte Corp. is an equal opportunity employer. If selected for an interview, please advise our Human Resources team if you require accommodation during the interview and assessment process. We will work with applicants to accommodate all accessibility needs.