Job description

Key Responsibilities

• Design, develop, and implement machine learning models to address complex challenges in financial process automation.

• Partner with product managers and engineers to translate business needs into robust ML-driven solutions.

• Build and maintain scalable data pipelines, including data preprocessing, transformation, and feature engineering.

• Fine-tune model performance and ensure seamless integration into production environments.

• Continuously monitor deployed models and enhance them through A/B testing and performance analysis.

• Write clean, maintainable, and well-documented code, promoting reusability and clarity.

• Stay abreast of the latest advancements in AI/ML technologies and integrate relevant innovations into the product lifecycle.

• Provide mentorship to junior team members and help shape best practices in machine learning across the organization.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.

• 3–5 years of hands-on experience designing, training, and deploying ML models in production-grade systems.

• Proficiency in Python and key ML libraries such as TensorFlow, PyTorch, or scikit-learn.

• Strong foundation in supervised and unsupervised learning, including classification, regression, and natural language processing.

• Experience working with cloud-based ML platforms like AWS SageMaker, Azure ML, or Google Cloud Vertex AI.

• Solid command of SQL and experience with relational or analytical databases (e.g., BigQuery, Snowflake).

• Practical experience deploying models using Docker, Kubernetes, and API-based architectures.

• Familiarity with version control systems, CI/CD pipelines, and orchestration tools for ML workflows.

Preferred Qualifications

• Experience with MLOps practices and supporting tools.

• Background in working with financial datasets or enterprise SaaS platforms.

• Knowledge of A/B testing methodologies and experimentation frameworks.

• Familiarity with large language models (LLMs) or generative AI technologies is a plus.