Job Description

Key Responsibilities

Design, build, and deploy machine learning models for real-world applications. Collect, preprocess, and analyze large datasets to train ML algorithms. Implement and optimize algorithms for classification, regression, NLP, computer vision, or recommendation systems. Develop and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, evaluation, and deployment. Collaborate with data scientists, data engineers, and product teams to translate business requirements into ML solutions. Monitor model performance, retraining, and improvements using MLOps best practices. Develop APIs or microservices for model integration into production systems. Utilize cloud platforms (AWS/Azure/GCP) for scalable ML training and deployment. Document workflows, experiments, and technical designs. Stay current with new ML techniques, tools, and frameworks.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field. 2-5 years of experience in machine learning model development and deployment. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Solid understanding of data structures, algorithms, and statistical modeling. Experience with data manipulation tools (Pandas, NumPy, Spark). Knowledge of cloud platforms (AWS Sagemaker, Azure ML, GCP Vertex AI). Experience building and deploying ML models into production environments.

Preferred Qualifications

Experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes). Knowledge of deep learning, NLP, computer vision, or reinforcement learning. Experience with distributed training and large-scale data processing. Familiarity with CI/CD for ML and version control tools (Git). Experience with feature stores and model monitoring systems. Advanced degree (Master's/PhD) is a plus.
Job Type: Full-time

Pay: $77,829.24-$197,301.98 per year