Role Overview:

Design, develop, and deploy machine learning and AI models to support advanced analytics and business intelligence initiatives.

Key Responsibilities:

- Build and optimize end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and model evaluation.

- Collaborate with data engineers to ensure data quality, scalability, and efficient data access for AI/ML workloads.

- Partner with business analysts and stakeholders to translate business problems into ML/AI solutions and measurable outcomes.

- Implement AI model monitoring, validation, and governance practices to ensure production performance and compliance.

- Stay current with emerging AI/ML technologies, frameworks (TensorFlow, PyTorch, scikit-learn), and best practices.

- Support experimentation, POCs, and feasibility studies for new AI/ML capabilities.

- Document models, algorithms, and technical implementations for knowledge transfer and audit trails.

Qualifications:

- Advanced proficiency in Python, R, or similar languages; familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn).

- Strong understanding of machine learning algorithms, statistical modeling, and data science fundamentals.

- Experience with cloud ML platforms (Azure ML, AWS SageMaker, Google Vertex AI) and model deployment pipelines.

- Knowledge of data warehouse/lake architecture and SQL for efficient data access.

- Familiarity with ML Ops practices, model versioning, and CI/CD for machine learning.

- Strong problem-solving skills and experience translating business requirements into technical solutions.