Machine Learning Engineer
Summary
Design, build, and deploy production machine learning models and scalable training/validation/inference pipelines, collaborating with data scientists, engineers, and product teams. Core stack: Python with scikit-learn/TensorFlow/PyTorch, MLflow/TFX/SageMaker, Airflow/Kubeflow, cloud platforms, Docker, and Git.
Location:
We are looking for a highly skilledMachine Learning Engineerto join our AI and data science team. In this role, you will design, develop, and deploy machine learning models and pipelines that power critical data-driven solutions across our organization. You’ll collaborate with data scientists, software engineers, and product teams to deliver intelligent systems at scale.
Responsibilities
Design and implement machine learning models for classification, regression, recommendation, NLP, or time-series forecasting tasks.
Develop, test, and maintain scalable ML pipelines for training, validation, and inference.
Collaborate with data engineers to build efficient data ingestion and feature extraction systems.
Optimize model performance using techniques like hyperparameter tuning, cross-validation, and regularization.
Deploy models to production using MLOps practices with tools like MLflow, TFX, or SageMaker.
Monitor and maintain the health of deployed models, updating them as needed.
Document ML experiments, metrics, and decisions.
Work closely with cross-functional teams to identify machine learning opportunities and define technical solutions.
Requirements
Bachelor’s or Master’s in Computer Science, Machine Learning, Data Science, or related field (Ph.D. a plus).
3–5+ years of hands-on experience building machine learning models in production.
Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
Experience with ML pipeline tools (e.g., Airflow, Kubeflow, MLflow).
Familiarity with cloud services (AWS, GCP, or Azure) and model deployment.
Solid understanding of statistics, data structures, and algorithms.
Experience with version control (Git), containerization (Docker), and CI/CD for ML.
Preferred Qualifications
Experience with NLP or computer vision projects.
Familiarity with big data tools (e.g., Spark, Hadoop).
Experience using GPU-accelerated training environments.
Details
