AI ML DevOps Engineer

AI/ML DevOps Engineer

Deutsche Bank's Technology, Data & Innovation (TDI) function is seeking an L2 AI/ML DevOps Engineer to join their global Innovation team. This role will focus on the daily operations and real-time support of AI/ML systems in production, leveraging cutting-edge technologies in financial services innovation.

About the Team

DB Technology is a global team of tech specialists committed to technical excellence and financial services innovation. Our India tech center is a key growing hub, dedicated to building a diverse workforce and providing excellent opportunities for talented engineers.

What We’ll Offer You

• Best-in-class leave policy
• Gender-neutral parental leaves
• 100% reimbursement under childcare assistance
• Sponsorship for industry-relevant certifications and education
• Employee Assistance Program
• Comprehensive Hospitalization Insurance
• Accident and Term life Insurance
• Complementary Health screening

Your Key Responsibilities

• Manage Incident, Service, Problem, and Change Management of Shared AI Platforms.
• Monitor production AI/ML models for performance, latency, accuracy, data drift, and model drift.
• Proactively troubleshoot production issues and collaborate with L3 engineers and data scientists for escalations.
• Automate model packaging, versioning, and rollbacks.
• Optimize resource allocation for cost-effective AI workloads.
• Detect and mitigate data drift affecting model performance.
• Troubleshoot model failures, latency issues, and deployment errors.
• Utilize containerization technologies like Docker to package models and dependencies.
• Develop and maintain CI/CD pipelines for automating testing, integration, and deployment of ML models.
• Implement version control for code and model artifacts.
• Establish monitoring solutions for performance and health of deployed models.
• Set up logging mechanisms for debugging and auditing.
• Optimize ML infrastructure for scalability and cost-effectiveness.
• Implement auto-scaling mechanisms for varying workloads.
• Enforce security best practices and ensure compliance with regulations.
• Oversee management of data pipelines and storage systems.
• Implement data versioning and lineage tracking.
• Collaborate with DevOps teams to align MLOps practices.
• Continuously optimize and fine-tune ML models.
• Identify and address system bottlenecks.
• Maintain clear and comprehensive documentation of MLOps processes and procedures.

Your Skills And Experience

• Excellent communication and presentation skills, highly organized and disciplined.
• Experienced in working with multiple stakeholders and maintaining good business relationships.
• Comfortable working in VUCA environments.
• Expertise required in: Google Cloud (GKE, Terraform, IAM, BigQuery, Cloud Shell, Cloud Storage), AI/ML (AI Agents, ML models, Vertex AI, AutoML, BigQuery ML), MLOps & CI/CD Pipelines, Kubeflow, Vertex AI pipelines.
• Proficiency in designing, deploying, and managing AI agents (e.g., chatbots, virtual assistants).
• Familiarity with GCP Networking, Security concepts, VPC, Load balancers.
• Basic Unix server administration.
• Proficiency in Python, Shell Scripting, SQL.
• Familiarity with fine-tuning and deploying large language models on GCP.
• Understanding of security best practices, data governance, encryption, and AI regulations.
• Experience with GCP Cloud Logging, Cloud Monitoring, and AI Model Performance Tracking.
• 4+ years of IT work experience (AVP – 6+, Associate – 4+).
• Strong problem-solving skills and passion for AI research.
• Good inter-personal skills and ability to collaborate.

Educational Qualifications

• B.E. / B. Tech. / Master’s degree in Computer Science or equivalent.
• Added advantage: GCP Certifications, Kubernetes Certifications, AI/ML Educational background or Certifications.

How We’ll Support You

• Training and development opportunities.
• Coaching and support from experts.
• A culture of continuous learning.
• A range of flexible benefits.

Key skills/competency

• AI/ML DevOps
• Google Cloud Platform (GCP)
• Kubernetes
• CI/CD Pipelines
• Model Monitoring
• Data Drift Detection
• Python
• Shell Scripting
• Docker
• Large Language Models (LLMs)