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Senior MLOps Engineer
Il y a 2 mois
Cognism is a leading provider of international sales intelligence solutions. Our premium data has helped numerous global revenue teams transform their prospecting approach, resulting in predictable and prosperous outcomes.
We are at an exciting juncture in our growth, having successfully acquired Mailtastic (2020) and Kaspr (2022), a Paris-based sales prospecting tool. As we continue to expand, our primary objective is to hire individuals who align with our company values and culture.
Our values are the foundation of our organization, and we strive to create a diverse and inclusive global workplace that fosters personal growth and fun.
Your Role:
We are seeking an exceptional MLOps Engineer to join our Data team. This role involves hands-on engineering and MLOps responsibilities, with the individual reporting directly to the Engineering Manager in the Data team. The MLOps team at Cognism is responsible for optimizing and improving the quality of ML services and products. We are looking for a candidate who can advise and enforce best practices within the Data Science team, provide tooling and platforms that result in more reliable, maintainable, scalable, and faster Machine Learning workflows.
Your Responsibilities:
- Design and implement automation pipelines to operationalize the ML platform, model training, and model deployment.
- Develop and manage architectures, services, and pipelines on the AWS cloud that are secure, reliable, scalable, and maintainable.
- Contribute to MLOps best practices within the Science and Data team.
- Act as a bridge between AI, Engineering, and DevSecOps for ML deployment, monitoring, and maintenance.
- Collaborate with Data Scientists to provide tooling and integration of AI/ML models into larger systems and applications.
- Monitor and maintain production-critical ML services and workloads.
Your Experience:
Required:
- Strong understanding of cloud architectures and services fundamentals, AWS preferable, GCP, MS Azure.
- Good understanding of modern MLOps best practices.
- Good understanding of Machine Learning fundamentals.
- Good understanding of Data Engineering fundamentals.
- Experience with Infrastructure as Code (IaC) tools like Terraform, CDK, or similar.
- Experience with CI/CD pipelines (GitHub Actions, Circle CI, or similar).
- Basic understanding of networking and security practices on cloud.
- Experience with containerization (Docker, AWS ECS, Kubernetes, or similar).
- Proficiency in reading and writing Python code.
- Experience deploying and monitoring machine learning models on the Cloud in production.
- Fluent in English, good communication skills, and ability to work in a team.
- Enthusiasm in learning and exploring modern MLOps solutions.
Ideal:
- 3+ years in a MLOps, Machine Learning Engineer, or DevOps role.
- Ability to design and implement cloud solutions and build MLOps pipelines (AWS, MS Azure, or GCP) with best practices.
- Good understanding of software development principles and DevOps methodologies.
- Experience and understanding of MLOps concepts: Experiment Tracking, Model Registry & Versioning, Model & Data Drift Monitoring.
- Working with GPU-based computational frameworks and architectures on cloud (AWS, GCP, etc.).
- Knowledge of MLOps and DevOps tools: MLflow, Kubeflow, Metaflow, Airflow, or similar. Visualisation tools – Grafana, QuickSight, or similar. Monitoring tools – Coralogix or GrafanaCloud or similar. ELK stack (Elasticsearch, Logstash, Kibana).
- Experience working in big data domains (10M+ scales).
- Experience with streaming and batch-processing frameworks.
Bonus:
- Experience with MLOps Platforms (SageMaker, VertexAI, Databricks, or other).
- Experience reading and writing in Scala.
- Knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.
- Experience with SQL, NoSQL databases, data lakehouse.
- Please send your CV in English version.
We look forward to hearing from you.
