EYManager - Tech Consulting - National - CNS - TC - AI AND DATA - Bangalore
About this role
Requisition Id: 1729146
As a global leader in assurance, tax, transaction and advisory services, we hire and develop the most passionate people in their field to help build a better working world. This starts with a culture that believes in giving you the training, opportunities and creative freedom. At EY, we don't just focus on who you are now, but who you can become. We believe that it’s your career and ‘It’s yours to build’ which means potential here is limitless and we'll provide you with motivating and fulfilling experiences throughout your career to help you on the path to becoming your best professional self.
The opportunity : Manager-National-Tech Consulting-CNS - TC - AI AND DATA - Bangalore
National :
National comprises of sector agnostic teams working across industries for a well rounded experience.
CNS - TC - AI AND DATA :
EY Consulting is building a better working world by transforming businesses through the power of people, technology and innovation. Our client-centric approach focuses on driving long-term value for our clients by solving their most strategic problems. EY Consulting is made up of three sub-service lines: Business Consulting (including Performance Improvement and Risk Consulting), Technology Consulting and People Advisory Services.
In Tech Consulting, we are transforming businesses through the power of people, technology and innovation. It places humans@center, leverages technology@speed and enables innovation@scale.
Your key responsibilities
Technical Excellence
Automated Data Pipelines & Data Engineering
Experience: 6-9 years
- Design, build and maintain scalable, fault-tolerant data pipelines for batch and real-time workloads using Apache Beam, Dataflow, Pub/Sub, and BigQuery.
- Perform complex data manipulation and transformation to prepare training datasets, feature stores, and serving datasets for ML models.
- Enforce data quality, lineage, and observability standards across all pipelines.
- Implement and manage data orchestration with tools such as Cloud Composer (Airflow) or Vertex AI Pipelines.
CI/CD Pipelines & MLOps
- Architect and implement end-to-end MLOps workflows: data ingestion → model training → evaluation → deployment → monitoring → retraining
- Design and implement end-to-end MLOps pipelines using Vertex AI Pipelines or Kubeflow for automated training, evaluation, and retraining.
- Build CI/CD workflows using Cloud Build and GitHub Actions for model versioning, automated testing, and deployment gating.
- Manage the model lifecycle using Vertex AI Model Registry — including versioning, A/B testing, canary deployments, and rollback strategies.
- Set up model monitoring for prediction drift, data drift, and performance degradation using Vertex AI Model Monitoring.
GCP Deployment & Infrastructure
- Deploy forecasting APIs and batch inference services on Cloud Run, GKE, or Vertex AI Endpoints at scale.
- Provision and manage GCP infrastructure using Terraform (IaC); enforce IAM policies, VPC configurations, and Secret Manager for security.
- Optimise infrastructure for cost, performance, and scalability — leveraging spot/preemptible instances, autoscaling, and right-sizing strategies.
- Build dashboards and reporting layers using Looker or Looker Studio for business consumption of forecasting outputs.
Solution Architecture & Infrastructure Cost Estimation
- Own the end-to-end solution architecture — from data ingestion through to inference serving — on GCP.
- Produce detailed architecture diagrams (C4, sequence, data-flow) and technical design documents for stakeholder review and engineering alignment.
- Develop accurate infrastructure cost estimates for both development and production environments using GCP Pricing Calculator, prior benchmarks, and resource profiling.
- Present cost-vs-performance trade-off analyses and recommend optimal configurations aligned with business objectives and budget constraints.
- Evaluate build-vs-buy decisions for ML platform components and maintain a reusable architecture reference library.
Team Leadership & Mentoring
- Lead, mentor, and upskill a team of 4–8 data engineer or ML engineers; conduct code reviews and set technical standards.
- Define the team's technical roadmap, architectural patterns, and best practices for ML development and MLOps.
- Foster a culture of Innovation, experimentation, continuous learning, and engineering excellence within the team.
What we offer
Fuelled by the brilliance of our people, EY has emerged as the strongest brand and the most attractive employer in our field, with market-leading growth over competitors. Our people work side-by-side with market-leading entrepreneurs, game-changers, disruptors, and visionaries. As an organization, we are investing more time, technology, and money than ever before in skills and learning for our people. At EY, you will have a personalized Career Journey and also the chance to tap into the resources of our career frameworks to better know about your roles, skills, and opportunities.
EY is equally committed to being an inclusive employer, and we strive to achieve the right balance for our people—enabling us to deliver excellent client service while allowing our people to build their careers as well as focus on their wellbeing.
If you can confidently demonstrate that you meet the criteria above, please contact us as soon as possible.
Join us in shaping the future with confidence. Apply Now
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