EY - GDS Consulting - AIA - ML Ops Engineer - Senior | ErnstYoung Job

Job Description

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

MLOps / DevOps Engineer

AI Platform Engineering, CI/CD, LLMOps and Cloud Operations

Career Family       AIA - MLOps / DevOps Engineer

Role Type  Full Time

The Opportunity

We are seeking an MLOps / DevOps Engineer to build and operate secure, scalable and reliable delivery platforms for AI/ML and Generative AI workloads. The role covers CI/CD, infrastructure as code, container orchestration, model and prompt lifecycle automation, environment management, observability, security, release governance and production support across cloud-native platforms.

The successful candidate should be able to discuss enterprise, pre-production or production implementations, including the business context, individual contribution, technical design, controls, delivery approach and outcomes. Certifications and personal demonstrations supplement, but do not replace, implementation experience.

Key Responsibilities

• Design and implement CI/CD pipelines for application code, data pipelines, ML models, prompts, agent configurations and infrastructure.
• Build standardized development, test, staging and production environments using infrastructure as code and automated provisioning.
• Containerize AI services and deploy them using Kubernetes, OpenShift, Cloud Run, Azure Container Apps, serverless or equivalent managed platforms.
• Implement model, artifact, dataset, prompt and configuration versioning, lineage, registry and controlled promotion across environments.
• Automate model training, validation, packaging, deployment, rollback, retraining and scheduled or event-driven workflows.
• Enable LLMOps practices for prompt management, model endpoint configuration, evaluation gates, agent releases, observability and cost controls.
• Implement monitoring, logging, distributed tracing, alerting, SLOs and dashboards for infrastructure, APIs, data pipelines, models, retrieval systems and agents.
• Establish security controls covering secrets management, identity and access, network security, image and dependency scanning, vulnerability remediation, audit logging and policy enforcement.
• Design high-availability, backup, disaster recovery, scaling and performance patterns for production AI platforms.
• Integrate source control, artifact repositories, model registries, test automation, quality gates and approval workflows.
• Troubleshoot deployment, networking, IAM, pipeline, container, platform, model-serving and performance issues in collaboration with engineering and security teams.
• Create platform documentation, architecture diagrams, runbooks, operational procedures and release evidence; support incident, change and problem management.
• Promote reusable platform components, self-service templates and DevSecOps standards; coach delivery teams on engineering and operational practices.

Skills and Attributes

Professional Experience

• 4-8 years of experience in DevOps, cloud engineering, platform engineering, site reliability engineering, data engineering or MLOps.
• Hands-on experience supporting production-grade cloud platforms, CI/CD pipelines and containerized workloads.
• Experience operationalizing AI/ML or GenAI solutions, model-serving endpoints, data pipelines or API-based services is strongly preferred.

Cloud and Platform Skills

• Strong experience with at least one cloud platform: Microsoft Azure, AWS or GCP; working knowledge of its networking, IAM, compute, storage, monitoring and managed AI services.
• Hands-on experience with Docker and Kubernetes or OpenShift; experience with serverless and managed container platforms is beneficial.
• Strong experience with GitHub Actions, GitLab CI, Azure DevOps, Jenkins or equivalent CI/CD tooling.
• Experience with Terraform, Bicep, ARM, CloudFormation or equivalent infrastructure-as-code tools and configuration management.
• Working knowledge of Linux, shell scripting, Python, YAML, JSON, REST APIs and automation.

MLOps, LLMOps and Operations Skills

• Understanding of the ML lifecycle, feature and data pipelines, experiment tracking, model registry, model serving, validation, drift, retraining and rollback.
• Exposure to MLflow, Vertex AI, Azure Machine Learning, Amazon SageMaker, Databricks or equivalent AI lifecycle platforms.
• Understanding of LLMOps, prompt and configuration versioning, evaluation gates, vector stores, retrieval services, agent deployments and AI observability.
• Experience with monitoring and logging platforms such as Azure Monitor, CloudWatch, Google Cloud Operations, Prometheus, Grafana, Splunk or equivalent tools.
• Knowledge of DevSecOps, secrets management, IAM, secure software supply chain, vulnerability scanning, policy as code, auditability and compliance.
• Understanding of reliability engineering, capacity planning, auto-scaling, performance testing, incident response, SLOs, backup and disaster recovery.

Soft Skills

• Strong troubleshooting, root-cause analysis, documentation and production-support skills.
• Effective collaboration with application, data science, security, infrastructure and client teams.
• Automation-first mindset, attention to operational quality and commitment to continuous improvement.

Education and Preferred Certifications

• Bachelor's or master's degree in Computer Science, Information Technology, Engineering or a related discipline.
• Relevant Azure, AWS, Google Cloud, Kubernetes, Terraform, Databricks, DevOps or AI/ML certifications are preferred.

Why Join Us

• Be at the forefront of AI-driven innovation across multiple client sectors.
• Work with global clients to deliver measurable business impact.
• Collaborate with AI experts, analytics leaders and industry specialists in a highly entrepreneurial environment.

What We Offer

EY Global Delivery Services (GDS) is a dynamic and truly global delivery network. We work across six locations - Argentina, China, India, the Philippines, Poland and the UK - and with teams from all EY service lines, geographies and sectors, playing a vital role in the delivery of the EY growth strategy.

• Continuous learning: Develop the mindset and skills to navigate whatever comes next.
• Success as defined by you: Access tools and flexibility so you can make a meaningful impact, your way.
• Transformative leadership: Gain insights, coaching and confidence to become the leader the world needs.
• Diverse and inclusive culture: Use your voice and help others find theirs.

EY | Building a better working world 

 
EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.  

 
Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.  

 
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