EY-GDS Consulting-AI And DATA- AI Architect-Associate Director
Ernst and Young6 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Partner with C-suite and senior business leaders to identify AI-led transformation opportunities and connect solutions to measurable business value.
- Lead consultative selling, RFP/RFI responses, pursuit activities, solution narratives, technical sections, client presentations, and value engineering workshops.
- Architect end-to-end AI, Generative AI, and Agentic AI solutions across Azure, AWS, and GCP.
- Design enterprise AI patterns involving RAG, autonomous agent workflows, LLMOps, scalable cloud-native deployments, and multi-cloud data strategies.
- Work with data engineering and data science teams on data models, pipelines, ML development workflows, and modern data architectures.
- Oversee AI/GenAI project delivery, maintain architecture integrity and engineering quality, and act as a technical escalation point.
- Review solution designs, code, and architectural artifacts while mentoring architects, AI engineers, and delivery teams.
- Develop POVs, accelerators, prototypes, demos, reusable assets, and reference architectures for AI innovation.
- Represent the practice in client discussions, internal leadership forums, and external industry events.
- Provide selective hands-on prototyping, write and review testable code, and guide adoption of MLOps, LLMOps, DevOps, observability, and secure deployment practices.
Requirements
- Extensive pre-sales and consultative experience diagnosing business problems, framing value propositions, and connecting AI capabilities to measurable business impact.
- 16+ years of professional experience, including 5–8 years architecting and deploying enterprise-scale AI/ML, GenAI, and Agentic AI solutions.
- Experience working with and managing global stakeholders and driving significant business outcomes.
- Strong RFP/RFI leadership experience, including technical writing, solution shaping, and client-facing architecture and value engineering workshops.
- Deep expertise in AI solutions using microservices, cloud-native architectures, Agentic AI, AI-ready data, RAG, agents, orchestration, and enterprise-scale LLM patterns.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
- Experience defining MLOps, LLMOps, and AgentOps architectures covering lifecycle automation, observability, governance, and responsible AI.
- Hands-on experience deploying AI/ML solutions on Azure, AWS, and/or GCP, including platform-native AI services.
- Strong experience orchestrating LLMs, vector databases, and agent frameworks across cloud platforms.
- Strong background in data modeling, SQL, Databricks, Snowflake, Synapse, machine learning, deep learning, NLP, and Generative AI.
- Advanced Python programming skills, with PySpark as an optional skill, for prototyping, validation, and reference implementations.
- Experience integrating enterprise security, identity, and authentication into AI/ML and LLM applications.
- Experience deploying AI/ML workloads on Kubernetes, Web Apps, Databricks, or similar cloud-native platforms.
- Working knowledge of MLOps, LLMOps, and AgentOps toolchains, including model lifecycle tooling, feature stores, batch inference, and real-time endpoints.
- Familiarity with Azure DevOps, GitHub Actions, Jenkins, Terraform, AWS CloudFormation, and related DevOps tools.
- Working knowledge of Responsible AI, governance, model risk management, and compliance.
- Experience delivering production-ready enterprise architectures and guiding delivery teams through implementation.
- Strong understanding of data strategy, cloud-native engineering, analytics modernization, and platform-driven transformation.
- Project and client management skills, solutioning and pre-sales experience, and familiarity with data mesh, data fabric, real-time analytics, and multi-cloud data strategies.
Benefits
- Support, coaching, and feedback from engaging colleagues.
- Opportunities to develop new skills and progress your career.
- Individual progression planning and control over personal development.
- Challenging and stimulating client assignments in an interdisciplinary environment focused on quality and knowledge exchange.
- Freedom and flexibility to handle the role in a way that suits the employee.
Tech Stack
Apache SparkAWSAzureDatabricksDockerGitGitHub ActionsGoogle Cloud PlatformJenkinsKubernetesMLflowPythonSnowflakeSQLTerraform
Categories
Solutions Engineering