EY - GDS Consulting - AIA - Gen AI NVIDIA - Senior

The opportunity

We are establishing an AI Research & Model Engineering Center of Excellence (CoE) to build next-generation AI / GenAI capabilities that accelerate enterprise AI adoption and create differentiated business value.

The CoE will focus on researching, engineering, validating, optimizing, and commercializing AI solutions that can be reused across multiple business domains and clients. Our vision is to transform innovative AI ideas into production-grade products, reusable accelerators, and AI-as-a-Service offerings.

This is a highly technical Individual Contributor (IC) role for engineers who are passionate about solving complex AI problems, building enterprise-grade AI platforms, and driving innovation from research to production.

Your key responsibilities

Technical Excellence:

• AI Engineering & Agentic AI Development
• 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.
• Support production operations including troubleshooting, performance tuning, root-cause analysis, and continuous improvement.

Skills and Attributes:

• Educational Background
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related discipline.
• Candidates from Tier-1 engineering institutions are preferred.

• Professional Experience
• 4–10 years of hands-on experience in Artificial Intelligence, Machine Learning, Data Science, or Generative AI with strong Python programming skills.
• Solid understanding of Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), model fine-tuning, and Agentic AI.
• Experience working with Azure, AWS, or GCP and modern AI engineering, deployment, and MLOps/LLMOps practices.
• Hands-on exposure to NVIDIA AI technologies such as NeMo, NIM, RAPIDS, GPU computing, inference optimization, or similar AI infrastructure is highly desirable.
• Strong analytical thinking, problem-solving ability, debugging skills, and a research mindset with a passion for continuous learning.
• Product mindset with the ability to build reusable, production-grade AI capabilities rather than one-off project solutions.
• Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.

• Soft Skills
• Strong analytical and problem-solving capabilities.
• Excellent communication and stakeholder management skills.
• Ability to translate complex business requirements into scalable technical solutions.
• Strong collaboration skills across engineering, product, and business functions.
• Commitment to engineering excellence, continuous learning, and innovation

Preferred Certifications

• NVIDIA GenAI and LLM focused, AI/ML

What Success Looks Like

In this role, you will:

• Build AI solutions that move seamlessly from prototype to enterprise-scale production.
• Develop evaluation and benchmarking frameworks that establish trust in