AI Engineer, Manager - Technology Consulting

At EY, we develop you with future-focused skills and equip you with world-class experiences. We empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams.

We work together across our full spectrum of services and skills powered by technology and AI, so that business, people and the planet can thrive together.

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The opportunity

EY DnA is the data and advanced analytics capability within EY Asia-Pacific, with over 500 specialist employees working across multiple industry sectors.

We implement information-driven strategies, data platforms and advanced data analytics solution systems that help grow, optimize and protect client organizations. We go beyond strategy and provide end to end design, build and implementation of real-life data environments and have some of the best architects, project managers, business analysts, data scientists, big data engineers, developers and consultants in the region.

We seek a AI Engineer Manager to lead the design and delivery of advanced AI solutions for clients in a consulting environment. This role is hands-on and client-facing, focused on building specialized AI systems (e.g., custom LLM-powered applications, semantic search engines, knowledge graph platforms) and ensuring successful implementation across multi-industry use cases. As a technical leader, you will bridge cutting-edge AI engineering with business needs, mentor junior engineers, and drive delivery excellence in a fast-paced forward-deployed practice. The scope spans end-to-end solution development from architecture to deployment with an emphasis on LLM fine-tuning, AI infrastructure optimization, and quality outcomes for clients.

Your Key Responsibilities

AI Solution Design & Development: Architect and build specialized AI systems tailored to client requirements, including custom NLP/LLM solutions, computer vision or data science pipelines, and retrieval-augmented generation (RAG) workflows for knowledge-intensive applications.
LLM Customization & Fine-Tuning: Fine-tune and adapt large language models to client-specific domains and tasks, leveraging open-weight models (e.g. Mistral, Llama2, Qwen) or proprietary APIs as needed. Optimize prompt designs and training workflows to maximize model performance while ensuring responsible AI usage.
Semantic Search & Knowledge Integration: Implement semantic search solutions and vector database integrations (e.g. FAISS, Pinecone) to enable intelligent retrieval for AI applications. Develop knowledge graphs, ontologies, and semantic layers to enrich AI systems with domain context and support reasoning.
Infrastructure & Performance Optimization: Manage and optimize GPU/accelerator infrastructure (on cloud platforms like AWS, Azure, GCP or on-prem NVIDIA clusters) to support high-performance model training and inference. Drive token efficiency, latency reduction, and cost optimization in AI pipelines (through techniques like model quantization, batching, and caching).
Client Project Delivery: Lead day-to-day project execution for AI engagements across multiple industries (e.g. finance, healthcare, manufacturing, public sector). Collaborate with client stakeholders to gather requirements and define technical solutions. Ensure on-time delivery of high-quality results that meet business objectives.
Technical Leadership & Collaboration: Provide hands-on technical leadership to a cross-functional delivery team (data engineers, ML engineers, developers). Perform code reviews, troubleshoot complex issues, and enforce best practices in software engineering, MLOps, and DevSecOps. Coordinate with data scientists, cloud architects, and industry SMEs to integrate AI solutions within broader client architectures.
Quality Assurance & Governance: Implement robust testing, validation, and monitoring for AI solutions (model performance, data quality, bias/fairness checks). Ensure compliance with data privacy/security and Responsible AI guidelines throughout solution design and deployment. Proactively address technical risks and maintain documentation and standards for reproducibility.

To qualify for the role, you must have

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. Advanced degrees or certifications in AI/ML (or cloud architecture) are a plus
Minimally 8+ years of experience in AI/ML engineering or software development, including hands-on building of ML models or AI-driven applications.
Strong proficiency in machine learning frameworks (Python, PyTorch or TensorFlow), data pipelines, and cloud-native development.
Experience working in consulting, professional services or client-facing environments.
Proven track record delivering AI solutions in a client-facing setting, ideally across multiple industries.
Demonstrated experience working with large language models (LLMs) or advanced NLP (transformers, generators).
Skilled in fine-tuning or customizing open-source models and integrating them into production systems.
Knowledge of prompt engineering, embeddings, and RAG patterns is essential
Solid understanding of GPU/accelerator computing (CUDA, distributed training, model serving optimization) and MLOps tools for model lifecycle management.
Experience orchestrating ML workflows using containerization (Docker, Kubernetes) and CI/CD pipelines.
Ability to translate business problems into technical approaches and communicate complex concepts clearly to non-technical stakeholders.
Experience leading technical teams or workstreams, with strong mentoring skills and a collaborative approach.
Adaptability working in cross-functional teams and managing technical tasks to meet project goals.

What We Look For

Highly motivated individuals with excellent problem-solving skills and the ability to prioritize shifting workloads in a rapidly changing industry. An effective communicat