Key Responsibilities

AI & Machine Learning Development

• Design, develop, and implement machine learning and deep learning models to solve complex business problems.
• Work on NLP, LLMs, and real-time data processing pipelines.
• Analyze large datasets to extract insights and inform model development.

MLOps & Model Deployment

• Build and maintain CI/CD pipelines for ML workflows.
• Automate model training, testing, deployment, and monitoring.
• Ensure models are scalable, reproducible, and version-controlled.

Infrastructure & Performance Optimization

• Optimize backend systems and model inference performance.
• Manage model serving infrastructure using tools like Docker, Kubernetes, or MLflow.

Collaboration & Integration

• Collaborate with data scientists, software engineers, DevOps, and product teams to integrate AI solutions into production systems.
• Participate in agile development cycles and contribute to sprint planning.

Monitoring & Governance

• Implement monitoring for model drift, performance, and data quality.
• Ensure compliance with ethical AI standards and data privacy regulations.

Documentation & Best Practices

• Maintain clear documentation of models, pipelines, and processes.
• Conduct code reviews and enforce best practices in MLOps and AI engineering.

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.

Experience

• 2+ years of experience in developing and deploying machine learning models.
• Experience with MLOps tools such as MLflow, Kubeflow, Airflow, or SageMaker.
• Proficiency in Python and familiarity with Java, JavaScript, or R.
• Hands-on experience with AI frameworks like TensorFlow, PyTorch, or scikit-learn.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with big data technologies (e.g., Spark, Hadoop).
• Experience with NLP, computer vision, or reinforcement learning is a plus.

Business Understanding

• Passion for innovation and emerging technologies.
• Interest in experimental tech such as AI/ML, IoT, or LLM features.
• Understanding of the banking or financial services industry and its digital transformation.