Advance your career at EY, a global leader committed to building a better working world. We're seeking a Technical AI Product Engineer to design and deploy cutting-edge AI systems. This role is perfect for individuals passionate about moving beyond experimentation to create AI-powered products that drive real-world business impact and decisions.

Join a dynamic team focused on next-generation AI platforms, including Generative AI and Agentic AI solutions. You'll manage the complete product lifecycle, from initial concept to deployment and ongoing optimization, within an enterprise environment. If you possess a blend of deep AI/ML expertise, robust engineering skills, and a product-oriented mindset, we encourage you to apply.

As a Technical AI Product Engineer, you will architect and develop production-grade Generative AI and Agentic AI solutions. This involves translating complex business challenges into measurable AI outcomes and defining key performance indicators for product success.

You will engineer intelligent systems at scale, building and refining retrieval-augmented generation (RAG) pipelines and developing agent-based AI systems. Deployment will occur on cloud-native architectures, with a preference for Azure. Furthermore, you'll manage the entire GenAI Operations lifecycle, including implementing advanced prompt engineering, fine-tuning strategies, and establishing robust evaluation frameworks and guardrails for reliability and safety.

Embrace responsible AI practices by embedding bias detection, explainability, and moderation into solutions. Ensure adherence to data privacy, governance, and compliance standards, aligning all systems with enterprise security and trust principles. Collaboration is key, as you'll partner with product, data, and engineering teams to prioritize AI features and influence enterprise AI architecture and best practices.

We are looking for candidates with a strong foundation in Python and SQL, coupled with hands-on experience in core GenAI patterns such as prompt engineering, RAG, embeddings, semantic search, and agentic workflows. Proficiency with AI/ML frameworks like PyTorch, TensorFlow, Hugging Face, LangChain, or Semantic Kernel is essential.

Experience deploying AI solutions on cloud platforms, particularly Azure (Azure OpenAI, Azure ML, Azure AI Search), is required. A solid understanding of data processing tools like Pandas, NumPy, Spark, or Databricks is also necessary. Familiarity with MLOps/GenAI Ops practices, including MLflow, CI/CD, Docker, and Azure DevOps, along with experience building APIs and integrating AI into applications, is critical.

Ideal candidates will also possess a strong grasp of monitoring, observability, and system reliability. While not mandatory, experience building agent-based or autonomous AI systems, cost optimization for LLM workloads, multi-modal AI, or frontend integration for AI-driven experiences would be a valuable asset. A Bachelor’s or Master’s degree in Computer Science, AI, Data Engineering, or a related field, along with 2+ years of experience in AI engineering, is required.