- About Job/Role

Job Title: AI Engineer
Location: Pune, India

Join Deutsche Bank's Indra Program – the central initiative focused on introducing and safely scaling AI across the bank. As an AI Engineer, you will play a key role in deploying machine learning models into production, managing scalable infrastructure, and ensuring secure, compliant, and efficient AI solutions. This role emphasizes collaboration with data scientists, DevOps teams, and software engineers, aiming to deliver high-impact AI solutions in a regulated financial environment.

The Indra program focuses on:

Identifying AI opportunities across banking operations
Deploying AI use cases to create real business value
Building a secure, scalable AI Workbench
Establishing AI controls while maintaining agility

- Roles and Responsibilities

Model Deployment:

Collaborate with data scientists to move ML models into production environments.
Implement A/B testing, canary releases, and other deployment strategies for safe rollouts.

Infrastructure Management:

Design and manage cloud/on-prem infrastructure for hosting ML models.
Use containerization tools like Docker for packaging and deployment.

CI/CD & Version Control:

Build and maintain CI/CD pipelines for automated testing and deployment of ML models.
Implement version control for code, model artifacts, and data.

Monitoring & Logging:

Set up performance monitoring and health-check dashboards for live ML models.
Configure logging systems for debugging and auditing purposes.

Scalability & Optimization:

Optimize ML systems for scalability and cost-efficiency.
Use auto-scaling techniques to manage dynamic workloads.

Security & Compliance:

Implement security best practices to protect data and models.
Ensure compliance with industry regulations and internal governance frameworks.

Data Management:

Oversee data pipelines and storage systems used for training and inference.
Implement data versioning and lineage tracking to maintain data integrity.

Cross-functional Collaboration:

Partner with software engineers, DevOps, and data scientists to align on requirements and goals.
Drive MLOps best practices within broader organizational processes.

Performance Tuning:

Continuously optimize and fine-tune models and infrastructure for better performance.
Identify and address bottlenecks in the system.

Documentation:

Maintain thorough documentation of all MLOps processes, infrastructure setup, and model deployment practices.
Create best practice guides and troubleshooting documents for internal use.

- Required Qualification

Education:

Bachelor’s, Master’s, or PhD in a technical field such as Computer Science, Mathematics, Physics, Economics, etc.

Experience:

2–4 years of hands-on experience in AI/ML, Data Science, or MLOps.
Strong coding skills in Python, JavaScript, or similar.
Experience with AI/ML frameworks such as scikit-learn, TensorFlow, or Apache Spark.
Practical experience in the finance or banking domain is a plus.

Technical Skills:

Proficiency in containerization (Docker) and cloud infrastructure management.
Familiarity with CI/CD tools and version control practices.
Understanding of security and compliance for AI systems.
Exposure to data pipeline management and storage technologies.

Find Your Dream Job

Filters

Latest Job Opportunities