AI Systems Engineer (Python)

Responsibilities:

• Model Development: Design, develop, and train machine learning models to address specific business needs, including regression, classification, clustering, and other supervised/unsupervised learning tasks.
• Data Preprocessing: Analyze and preprocess large datasets, performing data cleaning, feature selection, and extraction to prepare data for model training.
• Model Optimization: Tune and optimize models for performance, accuracy, and scalability, using techniques like hyperparameter tuning, regularization, and cross-validation.
• Deployment & Integration: Collaborate with software engineering teams to deploy ML models into production environments, ensuring seamless integration into existing systems.
• Performance Monitoring: Track and analyze the performance of deployed models, making iterative improvements as necessary and troubleshooting any issues related to model drift or performance degradation.
• Research & Development: Stay updated with the latest advancements in machine learning techniques, tools, and frameworks, and apply new technologies to improve existing systems or develop new solutions.
• Documentation: Maintain comprehensive documentation for models, data pipelines, and workflows to ensure clarity and reproducibility.

Requirements:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field. A PhD is a plus.
• Technical Skills:
  • Strong proficiency in Python and relevant ML libraries (TensorFlow, Keras, PyTorch, Scikit-learn).
  • Experience with data manipulation libraries (Pandas, NumPy) and data visualization tools (Matplotlib, Seaborn).
  • Familiarity with machine learning algorithms such as linear regression, decision trees, random forests, support vector machines, neural networks, and deep learning models.
  • Experience working with large datasets and cloud platforms (AWS, GCP, or Azure) for model training and deployment.
  • Familiarity with MLOps practices for continuous integration and delivery of machine learning models.
• Desirable: Experience with natural language processing (NLP), computer vision, or reinforcement learning is a plus.
• Personal Attributes:
  • Strong analytical and problem-solving skills.
  • Ability to work independently and as part of a team.
  • Good communication and collaboration skills.

Benefits:

• Competitive salary and performance-based bonuses.
• Opportunities for professional growth and career development.
• Collaborative work environment with exposure to state-of-the-art ML techniques.
• Flexible work arrangements (remote/hybrid options available).