Description

You will design, implement, and maintain machine learning pipelines to enable model development and deployment at scale.

Responsibilities

• Build and maintain automated data pipelines for collection, preprocessing, and transformation.
• Productionize ML models by collaborating with data scientists and software engineers to ensure scalability and reliability.
• Implement MLOps practices, including CI/CD for models, versioning, monitoring, and automated testing.
• Optimize model training and inference using distributed computing, parallel processing, and GPU acceleration.
• Manage deployed models through performance tracking, versioning, and retraining cycles.

Required Skills

• 5+ years of experience as a Machine Learning Engineer or Software Engineer focused on scaling ML models.
• Proficiency in Python or Java.
• Hands-on experience with TensorFlow, PyTorch, Scikit-learn, or Keras.
• Experience building scalable pipelines using Airflow, Kubeflow, or similar tools.
• Proficiency with cloud platforms including AWS, GCP, or Azure.
• Experience with containerization using Docker and orchestration via Kubernetes.
• Knowledge of MLOps, including CI/CD, model versioning, and monitoring.
• Strong understanding of ETL processes and data processing frameworks like Spark or Hadoop.
• Experience with relational and non-relational databases (SQL, NoSQL).

Preferred Skills

• Experience with deep learning architectures like CNNs, RNNs, or Transformers.
• Familiarity with distributed computing frameworks such as Dask, Ray, or Horovod.
• Knowledge of big data tools including Apache Spark, Kafka, and Cassandra.

Key Skills

Education

Bachelor’s or Master’s in Computer Science