Job Openings AI Engineer / Data Scientist

About the job AI Engineer / Data Scientist

Role summary:
As an AI Engineer / Data Scientist, you will design, develop, and deploy machine learning models and data-driven solutions that power product features and business decisions. Youll work across the stack from data collection and feature engineering to model development, evaluation, and production deployment while collaborating with engineers, product managers, and stakeholders.


Key responsibilities

  • Collect, clean, and explore large structured and unstructured datasets to identify patterns and opportunities.

  • Design and implement machine learning models (supervised, unsupervised, deep learning) to solve business problems.

  • Develop robust feature engineering pipelines and maintain data quality.

  • Train, tune, and evaluate models using appropriate metrics and validation strategies.

  • Productionize models: build scalable inference pipelines, monitoring, and automated retraining where needed.

  • Write clean, tested code and integrate models into product/engineering systems.

  • Translate complex technical findings into clear, actionable insights for non-technical stakeholders.

  • Collaborate with cross-functional teams to define success metrics and measure model/business impact.

  • Stay current with ML/AI research and best practices.


Minimum qualifications

  • Bachelors degree in Computer Science, Statistics, Mathematics, Engineering, or related field (or equivalent practical experience).

  • 2+ years experience building machine learning models or data-driven products (industry or research).

  • Strong Python skills and experience with data tools/libraries (pandas, NumPy, scikit-learn).

  • Experience with at least one deep learning framework (TensorFlow, PyTorch, JAX).

  • Solid understanding of statistics, ML fundamentals, model evaluation, and validation.

  • Experience with SQL and working with relational databases or data warehouses.

  • Experience with version control (git) and collaborative development workflows.

  • Ability to communicate technical results to stakeholders.


Preferred (nice-to-have)

  • Experience deploying models to production (Docker, Kubernetes, cloud services like AWS/GCP/Azure).

  • Experience with NLP (transformers), computer vision, or time-series forecasting (depending on product).

  • Experience with big data technologies (Spark, Dask) and data engineering fundamentals.

  • Masters or PhD in a relevant field or relevant publications/open-source contributions.