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We are seeking a highly skilled and experienced Senior Machine Learning Developer to join our growing data and engineering team. The ideal candidate will have 5+ years of hands-on experience designing, developing, and deploying machine learning solutions from concept to production. This role requires strong expertise in machine learning algorithms, data engineering, and scalable system design, along with the ability to collaborate cross-functionally and mentor junior team members.
Job Responsibility:
Lead the end-to-end machine learning lifecycle: problem definition, data collection, feature engineering, model development, evaluation, deployment, and monitoring
Design, develop, and deploy scalable machine learning models in production environments
Build and maintain robust data pipelines for structured and unstructured data
Perform data analysis, experimentation, and model validation to ensure high performance and reliability
Optimize model performance, scalability, and inference speed
Collaborate closely with product managers, data engineers, software developers, and stakeholders to translate business requirements into ML solutions
Implement MLOps best practices, including CI/CD for ML models, versioning, monitoring, and retraining strategies
Ensure data security, governance, and compliance with relevant regulations
Conduct code reviews and maintain high coding standards and documentation
Research emerging ML techniques and recommend improvements or new solutions
Mentor junior machine learning engineers and provide technical leadership
Communicate progress, risks, and mitigation strategies to project managers and leadership
Requirements:
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field
5+ years of professional experience in machine learning development
Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, or XGBoost
Solid understanding of supervised and unsupervised learning, deep learning, and statistical modeling techniques
Experience with data preprocessing, feature engineering, and model evaluation methodologies
Hands-on experience with large datasets and distributed computing frameworks (Spark, Hadoop)
Strong experience deploying models in production environments using REST APIs or microservices
Familiarity with MLOps tools such as MLflow, Kubeflow, or SageMaker
Experience with cloud platforms such as AWS, Azure, or GCP
Strong understanding of databases (SQL and NoSQL)
Experience with version control systems (Git) and CI/CD pipelines
Excellent analytical, problem-solving, and debugging skills