Overview

EY - GDS Consulting - AIA - Gen AI NVIDIA - Senior At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you.

Responsibilities

• Design, develop, evaluate, validate, benchmark, and optimize AI and Generative AI solutions for enterprise-scale applications. Evaluate and compare foundation models, open-source LLMs, commercial models, SLMs, multimodal models, and Agentic AI frameworks to identify the best-fit solution for different business scenarios.
• Define model selection criteria by considering factors such as accuracy, reasoning capability, latency, inference cost, scalability, security, explainability, governance, deployment complexity, and total cost of ownership.
• Build repeatable evaluation methodologies and benchmarking frameworks to measure model quality, business performance, and production readiness.
• Design and implement production-grade AI applications by taking solutions through the complete lifecycle—from research and experimentation to Proof of Concept (PoC), pilot, production deployment, and continuous optimization.
• Develop and fine-tune LLMs, RAG architectures, AI agents, and domain-specific AI models to improve performance, reliability, and business outcomes.
• Build reusable AI platforms, SDKs, APIs, accelerators, and AI-as-a-Service capabilities that can be leveraged across multiple projects and clients. Engineer AI solutions with a product mindset, ensuring they are reusable, scalable, maintainable, and suitable for commercialization and monetization.
• Optimize AI workloads for cloud and GPU environments to improve inference performance, resource utilization, scalability, and operational efficiency. Research emerging AI technologies and rapidly build prototypes to evaluate their technical feasibility and business impact.
• Collaborate with architects, product managers, engineers, and business stakeholders to deliver innovative AI solutions that solve real-world business challenges. Contribute to enterprise AI standards, reference architectures, engineering best practices, governance frameworks, and reusable design patterns. Backend & Platform Engineering
• Design and build scalable backend services using Python, FastAPI, REST APIs, microservices, and event-driven architectures.
• Develop reusable AI platform components, services, APIs, and integrations to accelerate enterprise AI adoption.
• Integrate AI solutions with enterprise systems, third-party applications, workflow platforms, and data services. Troubleshoot and optimize AI pipelines, APIs, vector stores, backend services, and cloud-native applications.
• Implement scalable deployment strategies using containerized and cloud-native architectures. Cloud, Infrastructure & DevOps
• Develop enterprise AI solutions using Azure OpenAI, Azure AI Services, and cloud-native services.
• Deploy and manage applications using Docker, Kubernetes, OpenShift, and container orchestration platforms.
• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, and modern DevOps tooling.
• Ensure production readiness through monitoring, observability, automated testing, release management, and operational excellence.
• Support deployment and lifecycle management across development, testing, staging, and production environments. AI Governance, Security & Responsible AI Implement enterprise controls for PII protection, data privacy, AI security, compliance, and responsible AI practices.
• Support AI governance initiatives through monitoring, auditability, access controls, and compliance frameworks. Contribute to AI observability practices, including monitoring model behavior, hallucination risks, accuracy, latency, and retrieval quality.
• Ensure adherence to enterprise architecture, security standards, and engineering best practices. Team Collaboration & Delivery Excellence
• Collaborate with data engineers, cloud and platform teams, security teams, product owners, and business stakeholders to refine requirements and deliver scalable AI solutions.
• Participate in architecture reviews, code reviews, testing reviews, and technical design discussions.
• Drive engineering excellence through reusable components, documentation, automation, and quality standards.
• Develop evaluation and benchmarking frameworks that establish trust in AI systems. Enable the organization to select the right AI model for every business problem through structured evaluation and evidence-based decision making.
• Create reusable AI platforms, accelerators, and engineering assets that improve delivery speed and quality.
• Develop commercial-grade AI capabilities that can be productized, monetized, and adopted across multiple clients and business functions. Why Join Us
• Be at the forefront of AI-driven innovation across multiple client sectors.
• Work with global clients to drive real business impact.