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 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.

Key Responsibilities

Build and manage automation pipelines to operationalize the ML platform, model training and model deployment on AWS
Design and implement architectures, service and pipelines on the AWS cloud that are secure, reliable, scalable and maintainable
Operate, maintain and evolve our traditional ML systems in production: gradient-boosted tree models, embedding-based matching, fine-tuned transformer classifiers, and classic NLP pipelines, running on our own serving and data infrastructure as well as some LLM based workflows
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
Work closely with Data Scientists to provide tooling and integration of ML models into larger systems and applications
Monitor and maintain production critical ML services and workloads at scale

Your Experience

Required

Strong understanding of AWS cloud architecture and services
Experience deploying and monitoring classical ML models in production on AWS
Good understanding of modern MLOps best practices
Good understanding of core Machine Learning fundamentals (classical model training, evaluation, feature engineering not just imited to LLMs)
Experience serving models in production via a model-serving runtime (e.g. ONNX Runtime, NVIDIA Triton, TorchServe, or similar)
Experience with vector databases / approximate-nearest-neighbor search (e.g. Milvus, FAISS, pgvector, or similar)
Good understanding of Data Engineering fundamentals
Experience with Infrastructure as Code (Terraform, AWS 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
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 in AWS
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:

Kubeflow, Metaflow, Airflow or similar

Visualisation tools – Grafana, QuickSight or similar

Monitoring tools – Coralogix or GrafanaCloud or similar

ELK stack (Elasticsearch, Logstash, Kibana)ll,l

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)