Your key responsibilities
- Cloud and AI Solution Design
- Design and develop cloud-based AI architectures, ensuring scalability, security, and cost-efficiency.
- Integrate AI/ML frameworks with existing cloud platforms (e.g., AWS, Azure, GCP).
- Develop and maintain reusable AI models and components for various business use cases.
- AI/ML Deployment and Optimization
- Implement, deploy, and monitor machine learning models on cloud infrastructure.
- Optimize AI pipelines for real-time processing and batch operations.
- Ensure compliance with data privacy and security regulations.
- Cloud Infrastructure Management
- Design cloud-native solutions with a focus on reliability, performance, and high availability.
- Leverage cloud services (e.g., AWS SageMaker, Azure AI, GCP AI Hub) to implement AI workloads.
- Establish and maintain CI/CD pipelines for ML models.
- Collaboration and Stakeholder Management
- Collaborate with data scientists, engineers, and business leaders to identify AI-driven opportunities.
- Translate business needs into technical AI solutions.
- Provide technical leadership and mentorship to the engineering team.
- Research and Innovation
- Stay updated on the latest advancements in AI/ML and cloud technologies.
- Experiment with emerging AI tools and frameworks to drive innovation.
- Lead the adoption of best practices for AI model lifecycle management.
Skills and attributes for success
- Hands on expertise in cloud platforms: AWS, Azure, GCP.
- Hands on proficiency in AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Sound understanding on the Agentic frameworks
- Deep understanding on the transformers and encoder architectures
- Must have developed AI solutions
- Strong understanding of data engineering, ETL pipelines, and big data tools (e.g., Apache Spark, Hadoop).
- Hands-on experience with containerization and orchestration tools (Docker, Kubernetes).
- Knowledge of DevOps practices for AI/ML (MLOps).
- Strong problem-solving and analytical skills.
- Excellent communication and stakeholder management abilities.
- Ability to work in a fast-paced, collaborative environment.
Preferred Qualifications:
- Certifications in cloud platforms (e.g., AWS Certified Solutions Architect, Azure AI Engineer) are a plus.
- Experience:
- Proven experience designing and implementing cloud-based AI solutions.
- Strong background in machine learning, deep learning, and data modelling.
- Experience in distributed systems and microservices architecture.
- Agile Methodologies : Familiarity with Agile development practices and methodologies.
Education:
- Bachelor’s or master’s degree in computer science, Engineering, or a related field.
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. From accountants to coders to advisory consultants, we offer a wide variety of fulfilling career opportunities that span all business disciplines. In GDS, you will collaborate with EY teams on exciting projects and work with well-known brands from across the globe. We’ll introduce you to an ever-expanding ecosystem of people, learning, skills and insights that will stay with you throughout your career.
- Continuous learning: You’ll develop the mindset and skills to navigate whatever comes next.
- Success as defined by you : We’ll provide the tools and flexibility, so you can make a meaningful impact, your way.
- Transformative leadership : We’ll give you the insights, coaching and confidence to be the leader the world needs.
- Diverse and inclusive culture: You’ll be embraced for who you are and empowered to use your voice to help others find theirs.
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.