ML Ops Lead - QuantumBlack

Key Skills

Apache Airflow, Argo, AWS, Azure, Databricks, DevOps, Docker, Github, GitLab, Google Cloud Platform (GCP), Jenkins, Kubeflow, Kubernetes-K8s, Machine learning techniques, MLflow, MySQL, Node.js, Postgres, Python Programming, SQL, Terraform, TypeScript

Job Description

WHO YOU'LL WORK WITH

You will join the Paris office and will be part of a Technical Delivery/MLOps team in QuantumBlack. 

You will work with software engineers, data scientists, data engineers, designers and integrative consultants on projects that address the topmost strategic priorities of our clients.

At QuantumBlack:

• We Create Real-World Impact– No project is ever the same; we work across multiple sectors, providing unique learning and development opportunities internationally.
• Fusing Tech & Leadership– We work with the latest technologies and methodologies and offer first class learning programs at all levels. 
• Multidisciplinary Teamwork– Our teams include data scientists, engineers, project managers, UX and visual designers who work collaboratively to enhance performance.
• Innovative Work Culture– Creativity, insight and passion come from being balanced. We cultivate a modern work environment through an emphasis on wellness, insightful talks and training sessions.
• Striving for Diversity– With colleagues from over 40 nationalities, we recognize the benefits of working with people from all walks of life.

WHAT YOU'LL DO

As an MLOps Lead in QuantumBlack you will oversee the development and deployment of technology that enables data scientists and data engineers to build, productionize and deploy machine learning models following best practice. 

You will work to set the standards for SWE and DevOps practices within multi-disciplinary delivery teams. You will engage with clients to understand their technology stack and then choose and use the right cloud services, DevOps tooling and ML tooling for the team to be able to produce high-quality code that allows your team to release to production. You will build modern, scalable, and secure CI/CD pipelines to automate development and deployment workflows used by data scientists (ML pipelines) and data engineers (data pipelines). 

In this role, you will shape and support next generation technology that enables scaling ML products and platforms, and bring expertise in cloud to enable ML use case development, including MLOps.

Our Tech Stack

We leverage AWS, Google Cloud, Azure, Databricks, Docker, Kubernetes, Argo, Airflow, Kedro, Python, Terraform, GitHub actions, MLFlow, Node.JS, React, Typescript amongst others in our projects.

QUALIFICATIONS

• Bachelor’s degree or higher required, preferably in the fields of computer science, IT, MIS, or engineering
• 6+ years industry experience
• 4+ years of experience contributing to the 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
• 4+ years of on-the-job experience working with data teams and automating ML and other data-intensive applications development workflows
• 2+ years in a technical lead role
• 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
• 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 2 major DevOps stacks (e.g., Azure DevOps, Gitlab, 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)
• Hands-on coding skills Python 3 (e.g., 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)
• 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