Role Summary
Seeking an experienced AI/ML Engineer with strong software engineering fundamentals and hands-on machine learning expertise to design, build, and scale intelligent solutions across banking and financial services. The ideal candidate will have 3–4 years of practical AI/ML experience, the ability to lead technical initiatives, and a passion for delivering secure, scalable, and business-impacting AI systems.
Key Responsibilities
Design, develop, and deploy machine learning and AI models for use cases such as fraud detection, credit risk, personalization, customer analytics, and process automation.
Lead end-to-end ML lifecycle activities: data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
Collaborate with product managers, data scientists, and business stakeholders to translate business problems into AI-driven solutions.
Provide technical leadership and mentorship to junior engineers and contribute to best practices in ML engineering.
Build and maintain production-grade ML pipelines using MLOps practices (CI/CD, model versioning, monitoring, retraining).
Ensure solutions meet enterprise standards for security, privacy, compliance, and model governance, especially in a regulated banking environment.
Optimize model performance, scalability, and reliability in cloud and distributed environments.
Contribute to architecture decisions and technical design reviews for AI platforms and services.
Required Qualifications
3–4 years of hands-on experience in AI/ML engineering or applied data science in production environments.
Strong programming skills in Python and experience with modern software engineering practices (OOP, design patterns, testing).
Solid understanding of machine learning algorithms, statistics, and model evaluation techniques.
Experience with ML frameworks such as TensorFlow, PyTorch, scikit-learn, or similar.
Proven ability to lead technical workstreams or guide a small engineering team.
Experience working with REST APIs, microservices, and containerization (Docker, Kubernetes preferred).
Strong problem-solving skills and ability to communicate technical concepts to non-technical stakeholders.
Preferred / Nice-to-Have
Experience in financial services, banking, or fintech environments.
Hands-on exposure to cloud platforms (AWS, Azure, or GCP), especially AI/ML services.
Knowledge of MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
Familiarity with data engineering tools and big data technologies (Spark, SQL, Databricks).
Understanding of model risk management, explainable AI (XAI), and regulatory expectations.
