Machine Learning Engineer

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.

Originally posted on Himalayas