Data Scientist / ML Engineer - Intermediate

As the Lead AI/ML Engineer/Data Scientist, you will be responsible for designing, developing, and implementing machine learning algorithms and AI solutions that address complex business challenges. You will lead a team of engineers collaborating with cross-functional teams to drive the successful deployment of AI initiatives. Your expertise will be crucial in shaping our AI strategy and ensuring the delivery of high-quality, scalable solutions.

Key Responsibilities:

• Lead the design and development of machine learning models and AI solutions to solve business problems.

• Select appropriate machine learning or deep learning models based on problem context and data availability.
• Develop, train, test, and validate models using state-of-the-art methodologies and frameworks.
• Collaborate with analytics, developers and domain experts to acquire, clean, and preprocess large datasets.
• Engineer features and performs exploratory data analysis to ensure data quality and model readiness.
• Containerize models (Docker/Kubernetes), implement monitoring (Prometheus/Grafana), and automate pipelines (MLflow/Kubeflow).
• Implement models into production environments, ensuring robustness, scalability, and maintainability.
• Develop and maintain CI/CD pipelines for seamless model integration and deployment.
• Monitor and evaluate model performance post-deployment and iterate based on feedback and performance metrics.
• Document model architecture, development processes, and performance evaluations thoroughly.
• Share insights and technical know-how with team members to foster a culture of continuous learning and improvement.
• Research & Innovation: Stay ahead of AI trends (LLMs, generative AI) and advocate for ethical AI practices.

• Analyze large datasets to extract insights and improve model performance.
• Ensure compliance with data privacy and security regulations in all AI/ML initiatives.

Qualifications

• Bachelors or masters degree in computer science, Data Science, Machine Learning, or a related field.
• Proven experience (5+ years) in AI/ML engineering, with a strong portfolio of successful projects.
• Proficiency in programming languages such as Python, R, or Java, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Strong understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).