OUR WORK MODEL

Hybrid: This is a hybrid role, requiring you to work from our location office 2–3 days per week, with flexibility to work remotely on other days.

YOUR ROLE

Cognism is actively seeking an outstanding MLOps Engineer to join our growing Data team. This role is primarily a hands-on engineering and MLOps position, with the individual reporting directly to the Engineering Manager in the Data team. The MLOps at Cognism is entrusted with optimizing and improving the quality of ML services and products. Advising and enforcing best practices within Data Science team, provide tooling and platforms that ultimately results in more reliable, maintainable, scalable and faster Machine Learning workflows. The successful candidate will be at the forefront of our MLOps initiatives, especially during the implementation of our machine learning platform and best practices.

As an MLOps Engineer, you will play a critical role in bridging the gap between machine learning development and robust production systems. You will collaborate closely with Data Scientists, ML Engineers, and DevOps teams to streamline the deployment, monitoring, and scaling of ML models. Your focus will be on building reliable, automated pipelines and infrastructure that ensure models are delivered to production efficiently, securely, and at scale.

• Building and managing automation pipelines to operationalize the ML platform, model training and model deployment
• Design and implement architectures, service and pipelines on the AWS cloud that are secure, reliable, scalable and maintainable
• Contributing to the MLOps best practices within the Science and Data team
• Acting as a bridge between AI, Engineering, and DevSecOps for ML deployment, monitoring, and maintenance
• Communicate and work closely with team of 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

• 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 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 the modern MLOps solutions

• 3+ years in a MLOps, Machine Learning Engineer or DevOps role
• Ability to design and implement cloud solutions and ability to build MLOps pipelines (AWS, MS Azure or GCP) with best practices
• Good understanding of software development principles, 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

• 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