EY - GDS Consulting - AIA - ML Ops - Senior

Job Title: Senior ML Ops Developer

Job Type: Full-time

Job Description

We are seeking a Senior ML Ops Developer to be the hands-on expert responsible for the end-to-end operationalization of machine learning models. The ideal candidate will have 5-8 years of experience building, automating, and maintaining robust, scalable, and secure ML pipelines in a cloud environment. This role requires deep proficiency in Python, containerization, Kubernetes, CI/CD, and model monitoring to ensure the reliability and performance of AI solutions within the banking and insurance sectors.

Key Responsibilities

Automation & Pipeline Execution

• Design, implement, and maintain fully automated ML Ops pipelines (CI/CD/CT) for model training, testing, deployment, and automated retraining, utilizing tools like Kubeflow, Airflow, or Azure/AWS native services.
• Own the deployment process, containerizing models using Docker and orchestrating scalable services via Kubernetes (AKS/EKS) to manage high-volume, low-latency inference endpoints.
• Build and manage sophisticated CI/CD pipelines (Azure DevOps, AWS Code Services, Jenkins) that ensure reproducibility by integrating code, data, and model versioning.
• Implement Infrastructure as Code (IaC) templates (e.g., Terraform) for the repeatable provisioning and configuration of ML infrastructure components.

Model Monitoring & Data Governance

• Implement comprehensive logging, monitoring, and alerting systems using tools like Prometheus, Grafana, or cloud-native monitors to track Model Drift, Data Quality, and prediction latency.
• Implement technical mechanisms for model versioning, experiment tracking (MLflow/DVC), and model lineage to meet audit and compliance requirements.
• Act as the Tier-3 escalation point for production issues, rapidly diagnosing and resolving problems related to model performance, infrastructure failures, or data pipeline interruptions.
• Enforce security best practices, including access control (RBAC), secrets management, and data encryption within the ML pipeline.

Required Skills & Experience

• 5-8 years of hands-on experience in DevOps, ML Engineering, or a dedicated ML Ops role, with a strong track record of deploying models into regulated production environments.
• Expert proficiency in Python and solid software engineering principles.
• Deep hands-on expertise in Docker and Kubernetes (AKS, EKS, or GKE).
• Proven experience building and managing automated CI/CD pipelines for ML models.
• Strong working knowledge of cloud platforms (Azure, AWS, or GCP) and their managed ML services (e.g., Azure ML, SageMaker, Vertex AI).
• Practical experience with Infrastructure as Code (Terraform) for cloud resource management.
• Experience with at least one major model and experiment tracking tool (e.g., MLflow, DVC, Weights & Biases).
• Strong understanding of data engineering concepts (ETL, data warehousing) and data pipeline tools (e.g., Airflow, Azure Data Factory, AWS Glue).

Preferred Skills

• Experience designing and implementing Feature Stores.
• Knowledge of advanced monitoring and data quality libraries (e.g., Evidently AI, Deepchecks).
• Familiarity with distributed computing frameworks (e.g., Spark).
• Experience working in the banking, financial services, or insurance (BFSI) domain.
• Professional certification in cloud (AWS, Azure) in ML/AI Specialty and orchestration technologies (e.g., Certified Kubernetes CKAD).

Soft Skills

• Exceptional ability to translate complex technical requirements and infrastructure decisions to non-ML experts (Data Scientists, Business teams, Product Managers).
• Work closely with Data Scientists to transition complex model artifacts into production-ready services, optimizing code for speed and scalability.
• Create and maintain detailed technical documentation for all ML Ops workflows, deployment runbooks, and platform standards.
• Proactive and systematic approach to troubleshooting production incidents, identifying root causes, and implementing preventative measures.
• Meticulous approach to documentation, configuration management, and maintaining robust security and compliance standards.
• High sense of ownership for the production environment and the ability to thrive in a fast-paced, agile environment with evolving technology stacks.
• Provide technical guidance and conduct code reviews for junior ML Ops and Data Engineering team members, promoting ML Ops best practices.