About this role
EY GDS seeks an Associate Director and Forward Deployed Engineer in Bengaluru to design, build and deploy AI-powered solutions directly with clients. This is a hands-on individual-contributor role, not a people-management or program-governance position. The team applies AI, automation and data engineering to business challenges across sectors including banking, insurance, manufacturing, healthcare, retail, automotive, supply chain and finance.
Responsibilities
- Embed with clients, clarify ambiguous business and technical problems, advise senior stakeholders and own solutions from discovery and prototype through production deployment.
- Build LLM-powered chatbots, copilots and assistants; Retrieval-Augmented Generation systems; and agentic workflows and orchestration pipelines. Integrate them with enterprise data platforms, APIs and external systems.
- Turn proofs of concept into scalable, reliable and observable enterprise systems. Evaluate latency, cost, accuracy, scalability and security, and iterate using client feedback.
- Develop cloud-native architectures on Azure, AWS or GCP; create reusable accelerators, frameworks, reference architectures and deployment practices; and work with internal teams on long-term support and scale.
Requirements
- At least 15 years of experience in software engineering, data engineering or AI/ML, with strong hands-on system-building and production-deployment experience.
- Proven delivery in client-facing or ambiguous environments; understanding of distributed systems, microservices, APIs and cloud-native architecture; ability to independently deliver complex solutions; and strong communication with technical and business stakeholders. The role calls for experience with GenAI, AI/ML and data science solutions across enterprise and hyperscaler technology stacks.
Preferred qualifications include hands-on work with LLM APIs, prompt engineering, RAG, vector databases and agentic orchestration; enterprise data-platform integration; and exposure to OpenAI, Claude, Cursor, Palantir, Azure, AWS or GCP. DevOps, CI/CD, infrastructure automation, MLOps, model monitoring, frontend development, prior forward-deployed or field-engineering work, and building early-stage AI or data products are also desirable.
In the first 6–12 months, success includes delivering 2–4 production-grade client AI use cases, showing measurable business impact and creating reusable patterns. The posting lists salary as competitive.