EY - GDS Consulting - AI and DATA - AI Architect - Associate Director
- Bangalore, Karnataka
- Permanent
- Full-time
- Partner with C-suite and senior business leaders to understand strategic priorities and identify AI-led transformation opportunities that deliver clear business value.
- Shape, articulate, build future architecture, and present compelling value propositions, solution narratives, and business cases for AI/GenAI/Agentic AI engagements.
- Drive account teams in pursuit cycles, contributing to RFP responses, solution design, storyline decks, and client presentations.
- Lead client discovery and ideation workshops to define AI use cases, roadmap, and ROI frameworks.
- Drive consultative selling by helping clients understand where AI can meaningfully impact operations, customer experience, finance, and industry-specific value chains.
- Architect end-to-end AI, Generative AI, and Agentic AI solutions across Azure (Azure AI, Azure OpenAI), AWS (SageMaker), and GCP (Vertex AI).
- Leverage leading AI platforms such as OpenAI, Azure OpenAI, Hugging Face, LangChain, vector databases (Pinecone, ChromaDB), and agent orchestration frameworks.
- Design architecture patterns for enterprise AI systems-including RAG, autonomous agent workflows, LLMOps, and scalable cloud-native deployments.
- Work with data engineering and data science teams to define data models, pipelines, and ML development workflows using Databricks, Synapse, Snowflake, Spark, Kubernetes, Docker, etc.
- Oversee delivery of AI/GenAI projects to ensure architecture integrity, engineering quality, and alignment to business value commitments.
- Act as the technical escalation point during delivery, guiding solution teams and ensuring adherence to best practices.
- Review detailed solution designs, code, and architectural artifacts to maintain technical rigor.
- Mentor and coach architects, AI engineers, and delivery teams on solution design, coding standards, and engineering excellence.
- Drive the development of POVs, accelerators, prototypes, and demos using Azure AI, OpenAI, NVIDIA AI, and related technologies.
- Create industry-specific and horizontal reference architectures, reusable assets, and solution frameworks.
- Represent the practice in client discussions, internal leadership forums, and external industry events on AI strategy and innovation.
- Lead by example with hands-on prototyping when required; write and review high-quality, testable code in Python and other languages.
- Guide teams in adopting modern practices for MLOps/LLMOps, DevOps, observability, and secure deployment.
- Extensive pre-sales and consultative experience with the ability to diagnose business problems, frame value propositions, and connect 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 on cloud or on-prem environments.
- Experience in working & managing global stakeholders.
- Should have driven significant business outcomes as a part of their role.
- Strong RFP/RFI leadership experience, including solution shaping, writing technical sections, and conducting client-facing architecture and value engineering workshops.
- Deep expertise in designing AI solutions using microservices and cloud-native architectures, with strong understanding of Agentic AI, AI-ready data, and enterprise-scale LLM solution patterns (RAG, agents, orchestration).
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field.
- Proven experience defining MLOps, LLMOps, and AgentOps architectures, including model lifecycle automation, observability, governance, and responsible AI.
- Hands-on experience deploying AI/ML solutions on Azure, AWS, and/or GCP, with strong understanding of platform-native services (Azure AI, Azure OpenAI, AWS Bedrock, GCP Vertex/Gemini).
- Strong experience using and orchestrating LLMs across cloud platforms-OpenAI, Azure OpenAI, Bedrock, Vertex AI, Gemini AI, vector DBs, and agent frameworks.
- Solid background in data modeling, SQL, and modern data architectures (Databricks, Snowflake, Synapse).
- Strong foundational understanding of machine learning, deep learning, NLP, and Generative AI.
- Advanced programming skills in Python (and optionally PySpark) for prototyping, validation, and reference implementations.
- Experience integrating enterprise-grade security, identity, and authentication into AI/ML and LLM-based applications.
- Proven expertise deploying AI/ML workloads on Kubernetes, Web Apps, Databricks, or similar cloud-native platforms.
- Strong working knowledge of MLOps / LLMOps / AgentOps toolchains-Git, MLflow, feature stores, CI/CD, batch inference, real-time endpoints.
- Familiarity with Azure DevOps, GitHub Actions, Jenkins, Terraform, AWS CloudFormation, and related DevOps tooling.
- Working knowledge of Responsible AI principles, governance, model risk management, and compliance.
- Demonstrated experience delivering enterprise-grade, production-ready 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.
- Experience in solutioning and pre-sales engagements.
- Exposure to data mesh principles and domain-oriented data product design.
- Familiarity with data fabric architectures and multi-cloud data strategies.
- Familiarity with real-time analytics
- Support, coaching and feedback from some of the most engaging colleagues around
- Opportunities to develop new skills and progress your career
- The freedom and flexibility to handle your role in a way that's right for you