MLOps Engineer

Cognism; Warszawa; On-site; PLN 180,000 - 240,000; Full time

Job summary

Cognism is seeking an outstanding MLOps Engineer to join our Data team in a hands-on role focused on building and operationalizing ML services on cloud platforms. You will design architectures on AWS/GCP/Azure, implement MLOps best practices, collaborate with data scientists, and ensure production ML workflows are reliable, scalable, and secure.

Requirements include strong Python, IaC (Terraform), containerization (Docker/Kubernetes), and CI/CD experience with GitHub Actions or CircleCI; fluent

Qualifications

• Must have strong understanding of MLOps concepts and cloud platforms.
• Proficient in Python and data engineering fundamentals.
• Experience with IaC, CI/CD pipelines, and containerization.
• Fluent in English and able to work collaboratively in a team.

Responsibilities

• Build and manage automation pipelines to operationalize ML platforms and deployments.
• Design secure, scalable cloud architectures (AWS/GCP/Azure) and pipelines.
• Contribute to MLOps best practices within the team.

Skills

MLOps concepts

Python programming

Team collaboration

English communication

Cloud fundamentals

CI/CD practices

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

AWS

Terraform

GitHub Actions

FastAPI

CircleCI

Job description

Cognism is the leading provider of European B2B data and sales intelligence. Ambitious businesses of every size use our platform to discover, connect, and engage with qualified decision-makers faster and close more deals. Headquartered in London with global offices, Cognism’s contact data and contextual signals are trusted by thousands of revenue teams to eliminate the guesswork from prospecting.

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 reportingdirectly to the Engineering Manager in the Data team. The MLOps at Cognism is entrusted withoptimizing and improving the quality of ML services and products. Advising and enforcing bestpractices within Data Science team, provide tooling and platforms that ultimately results in morereliable, maintainable, scalable and faster Machine Learning workflows. The successfulcandidate will be at the forefront of our MLOps initiatives, especially during the implementationof our machine learning platform and best practices.

Key Responsibilities:

• 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.

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 reading and writing Python code;
• Experience with API deployment frameworks such as FastAPI;
• 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.

Ideal:

• 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, MSAzure 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, GCPetc.);
• 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;
• Experience working in big data domains (10M+ scales);
• Experience with streaming and batch-processing frameworks.

Bonus:

• Experience with MLOps Platforms (Nvidia Triton, SageMaker, VertexAI, Databricks, or other);
• Knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.;
• Experience with SQL, NoSQL databases, data lakehouseerience.

WHO ARE WE

Cognism is the leading provider of European B2B data and sales intelligence. Ambitious businesses of every size use our platform to discover, connect, and engage with qualified decision-makers faster and close more deals. Headquartered in London with global offices, Cognism’s contact data and contextual signals are trusted by thousands of revenue teams to eliminate the guesswork from prospecting.

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 reportingdirectly to the Engineering Manager in the Data team. The MLOps at Cognism is entrusted withoptimizing and improving the quality of ML services and products. Advising and enforcing bestpractices within Data Science team, provide tooling and platforms that ultimately results in morereliable, maintainable, scalable and faster Machine Learning workflows. The successfulcandidate will be at the forefront of our MLOps initiatives, especially during the implementationof our machine learning platform and best practices.

Key Responsibilities:

• 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.

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 reading and writing Python code;
• Experience with API deployment frameworks such as FastAPI;
• 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.

Ideal:

• 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, MSAzure 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, GCPetc.);
• 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 (Nvidia Triton, SageMaker, VertexAI, Databricks, or other);
• Knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.;
• Experience with SQL, NoSQL databases, data lakehouseerience.

WHY COGNISM

At Cognism, we’re not just building a company - we’re building an inclusive community of brilliant, diverse people whosupport, challenge, and inspire each other every day. If you’re looking for a place where your work trulymakes an impact, you’re in the right spot!

Our values aren’t just words on a page—they guide how we work, how we treat each other, and how we grow together. They shape our culture, drive our success, and ensure that everyone feels valued, heard, and empowered to do their best work.

Here’s what we stand for:

We Own the Outcome Together. We Deeply Understand our Customers. We Celebrate Impact Wherever It Comes From.

At Cognism, we are committed to fostering an inclusive, diverse, and supportive workplace. We welcome applications from individuals typically underrepresented in tech, so if this role excites you but you’re unsure if you meet every requirement, we encourage you to apply!