1. Model Development & Implementation: Design, develop, train, and optimize robust and scalable machine learning models (e.g., deep learning, classical ML algorithms) for various applications.
2. Production Deployment (MLOps): Build and maintain end-to-end MLOps pipelines for model deployment, monitoring, versioning, and retraining, ensuring reliability and performance in production environments. 3. Data Engineering for AI: Work with large, complex datasets, performing data cleaning, feature engineering, and data pipeline development to prepare data for model training and inference. 4. Research & Prototyping: Explore and evaluate new AI/ML technologies, algorithms, and research papers to identify opportunities for innovation and competitive advantage. Rapidly prototype and test new ideas. 5. Performance Optimization: Optimize AI/ML models and inference systems for speed, efficiency, and resource utilization. 6. Collaboration & Communication: Partner closely with Data Scientists to transition research prototypes into production-ready solutions. Collaborate with Software Engineers to integrate AI models into existing products and platforms. Communicate complex AI concepts to both technical and non-technical stakeholders. 7. Code Quality & Best Practices: Write clean, maintainable, and well-documented code. Advocate for and implement software engineering best practices within the AI/ML lifecycle. 8. Monitoring & Maintenance: Implement robust monitoring for model performance, data drift, and system health in production, and troubleshoot issues as they arise.
Required qualifications to be successful in this role:
1. Hands-on experience as an AI Engineer, Machine Learning Engineer, or a similar role focused on building and deploying AI/ML solutions.
2. Strong proficiency in Python and its relevant ML/data science libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). 3. Extensive experience with at least one major deep learning framework such as TensorFlow, PyTorch, or Keras. 4. Solid understanding of machine learning principles, algorithms (e.g., regression, classification, clustering, ensemble methods), and statistical modeling. 5. Experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services (e.g., SageMaker, Azure ML, Vertex AI). 6. Proven experience with MLOps concepts and tools for model lifecycle management (e.g., MLflow, Kubeflow, Sagemaker MLOps, Azure ML Pipelines). 7. Strong SQL skills for data manipulation, analysis, and feature extraction from relational databases. 8. Experience with data preprocessing, feature engineering, and handling large datasets. 9. Familiarity with software development best practices including version control (Git), code reviews, and CI/CD. 10. Excellent problem-solving skills and the ability to work on complex, ambiguous problems. 11. Strong communication and collaboration skills to work effectively with cross-functional teams. #LI-SN1
Skills:
- Artificial Intelligence
- Statistical Analysis
