Senior Machine Learning Engineer

The Senior Machine Learning Engineer is a core technical leader in building, deploying and running scalable Al/ML solutions across the Bank. The role connects data science and software engineering. You will build reliable MLOps pipelines, move models into production and make sure they perform well, stay secure and meet the Bank's model risk and regulatory standards. You will also guide and grow other engineers in the team.

Job Profile (Roles & Responsibilities)

1. Model Development & Productionization

• Collaborate with Data Scientists and the Al & ML Architects to transition internal R&D prototypes into scalable, production-grade ML models.
• Design and implement end-to-end ML pipelines for data preprocessing, feature engineering, model training, and inference.
• Refactor data science code into modular, testable, and optimized Python microservices tailored for enterprise deployment.

2. MLOps & Infrastructure Management

• Architect and manage the MLOps lifecycle on cloud platforms, with a primary focus on Google Cloud Platform (GCP) and Vertex Al.
• Establish CI/CD pipelines for machine learning models, ensuring automated testing, deployment, and version control.
• Implement robust monitoring solutions to track model drift, data quality, and inference latency in a live production environment.

3. Integration & Collaboration

• Work closely with AI/ML/Data Architects and Tech Leads to integrate ML models into the Bank's legacy systems and digital channels.
• Optimize model inference performance to meet strict latency requirements for real-time customer journeys.
• Collaborate with the IT operations team to ensure containerized ML services (Docker, Kubernetes) are secure, maintainable, and scalable.

4. Governance & Compliance

• Ensure all ML deployments comply with the bank's strict security standards, data privacy regulations, and governance frameworks.
• Document model architectures, infrastructure configurations, and MLOps processes to maintain audit readiness.

Applicant Profile (Requirements & Qualifications)

1. Experience & Background

• Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, Mathematics, or a related field recognized by the University Grants Commission.
• 4+ Years of total experience in software engineering, data engineering, or machine learning.
• 3+ Years of hands-on experience as a Machine Learning Engineer, specifically focusing on model deployment and MLOps.
• Proven track record of deploying and managing ML models in a complex enterprise environment (Banking/Telco/Insurance is highly preferred).

2. Technical Competencies

• Core Languages: Expert proficiency in Python and SQL. Familiarity with Java/Spring Boot (the standard for our banking backends) is a strong plus.
• ML Frameworks and Algorithms: Deep knowledge of machine learning algorithms, libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
• Cloud & MLOps: Experience with Google Cloud Platform (GCP), Vertex Al, BigQuery, and Dataflow. Proficiency in MLOps tools (e.g., MLflow, Kubeflow) and CI/CD pipelines.
• Infrastructure: Experience with containerization and orchestration (Docker, Kubernetes, Cloud Run) and API development (REST/gRPC, FastAPI/Flask).

3. Mindset & Soft Skills

• Engineering Pragmatism: Focuses on building scalable, reliable, and secure solutions rather than just experimenting with the latest algorithms. Prefers robust configurations.
• Problem Solver: Ability to troubleshoot complex integration and performance issues across hybrid architectures (connecting Cloud services to On- Premise/Legacy systems).
• Collaborative Leader: Excellent communication skills with the ability to mentor junior engineers and effectively translate technical ML concepts to cross- functional stakeholders.
• Ownership: Takes end-to-end responsibility for models in production, from release to retirement.

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