EY GDS Consulting AIA ML Ops Senior
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Job Summary
At EY were all in to shape your future with confidence.
Well help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.
Join EY and help to build a better working world.
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.
EY Building a better working world
EY is building a better working world by creating new value for clients people society and the planet while building trust in capital markets.
Enabled by data AI and advanced technology EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance consulting tax strategy and transactions. Fueled by sector insights a globally connected multi-disciplinary network and diverse ecosystem partners EY teams can provide services in more than 150 countries and territories.
Key Skills
- Compliance Management
- Customer Retention
- Quality Assurance
- Customer Service
- Extensive Knowledge
- Account Management
- Front Office
- Telephone Calls
- Needs Analysis
- Cold calls
- Business Relationships
- Customer Complaints
- Sales Goals
- Sales Process
- New Clients
About Company
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