Senior machine learning engineer
Summary
Build and deploy ML models using Python, TensorFlow/PyTorch, and cloud tools; engineer features, optimize pipelines, and productionize AI systems with MLOps.
Key Responsibilities: Model Development & Optimization: Design, develop, and optimize machine learning models for real-world applications, ensuring high accuracy, scalability, and efficiency. ML Pipeline & Deployment: Build and maintain scalable ML pipelines using cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
Feature Engineering & Data Processing: Collaborate with data engineers to preprocess, clean, and transform large datasets for training and inference.
Productionization: Deploy ML models into production, monitor performance, and continuously improve them through A/B testing and retraining.
Collaboration: Work closely with cross-functional teams including software engineers, product managers, and business stakeholders to align ML solutions with business objectives.
MLOps & Automation: Implement MLOps best practices, automate model training and deployment, and ensure reproducibility.
Performance Monitoring: Develop and maintain monitoring tools to track model performance, drift, and reliability in production.
Research & Innovation: Stay updated with the latest trends and advancements in AI/ML, and integrate cutting-edge research into business solutions.
Required Qualifications & Skills:
Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph. D. is a plus.
Experience: Minimum 5+ years of experience in machine learning, deep learning, and AI model deployment in production environments.
Programming: Strong proficiency in Python, with experience in libraries like Tensor Flow, Py Torch, Scikit-learn, Pandas, and Num Py.
Cloud & Infrastructure: Hands-on experience with cloud services (AWS, GCP, Azure) and MLOps tools like Kubeflow, MLflow, or Sage Maker.
Big Data & Databases: Experience with Spark, Hadoop, SQL, and No SQL databases for handling large-scale datasets.
Dev Ops & CI/CD: Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines for ML model deployment.
Algorithm Development: Strong knowledge of ML algorithms, deep learning architectures (CNNs, RNNs, Transformers), and optimization techniques.
Problem-Solving: Strong analytical and problem-solving skills with the ability to design innovative ML solutions for complex business challenges.
Excellent Communication: Ability to explain technical concepts to non-technical stakeholders and document ML processes effectively.
Preferred Qualifications:
Experience with NLP, Computer Vision, or Reinforcement Learning.
Hands-on experience with Auto ML, hyperparameter tuning, and model interpretability.
Experience with real-time ML applications and edge AI.
Contributions to open-source ML frameworks or research publications. #J-18808-Ljbffr