Senior AI / Machine Learning Engineer

Streamline solution

Work with stakeholders to define ML-driven features and measurable success criteria.

Explore and analyze datasets (structured and unstructured) to extract insights and identify opportunities.

Design, train, and evaluate machine learning models for prediction, ranking, classification, clustering, or similar tasks.

Build robust data pipelines for feature engineering, training, and inference.

Deploy models into production (batch or real-time scoring) in collaboration with back-end and DevOps teams.

Monitor models in production: performance drift, data drift, latency, and failures.

Document experiments, decisions, and results in a way that the team can follow and reproduce.

Contribute to internal tools, libraries, and best practices for ML and data analytics.

Participate in technical design reviews, bringing a data and ML perspective to product discussions.

Mentor other engineers and share knowledge on ML, data, and analytics topics.

Required experience and skills

5+ years of experience in machine learning or applied data science, with production deployments.

Strong proficiency in Python and common ML/data libraries (for example: pandas, NumPy, scikit-learn; PyTorch or TensorFlow).

Solid knowledge of classical ML techniques (regression, tree-based models, clustering, model selection, evaluation metrics).

Experience working with large datasets, data warehouses or data lakes, and distributed processing frameworks (Spark, Dask, or similar).

Experience building data pipelines (e.g. Airflow, Dagster, Prefect, or similar tooling).

Experience deploying models into production (APIs, batch pipelines, or streaming) and monitoring them.

Strong understanding of experimentation, A/B testing, and how to interpret results.

Solid SQL skills and experience working with relational databases.

Ability to communicate complex findings in a clear, structured way to technical and non-technical audiences.

Experience working with engineers and product on turning data and models into product features.

Nice to have

Experience with MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).

Experience with big data stacks on AWS, GCP, or Azure (e.g. Redshift, BigQuery, S3-based data lakes).

Experience with modern LLMs and applied generative AI work.

Experience in a domain where data quality, reliability, and traceability matter (healthcare, finance, etc.).

Experience in a start-up or consulting environment, handling multiple projects or clients.