Senior Consultant - Forward Deployed Engineer, AI for AI Team

About the role

EY's GDS Managed Services "AI for AI" team helps clients solve complex business challenges with AI, automation and data engineering technology, serving sectors such as banking, insurance, manufacturing, healthcare, retail, supply chain and finance. It is looking for a Senior Consultant (Forward Deployed Engineer) with 3-5 years of hands-on engineering experience to design, build and deploy AI and GenAI solutions directly with clients. The role suits an individual contributor who builds real systems rather than manages teams.

Key responsibilities

• Lead complex end-to-end deployments in production at strategic customers
• Lead discovery, design, development and deployment of AI/GenAI solutions with client stakeholders
• Build LLM-driven applications, RAG systems and agentic workflows
• Create reusable accelerators, frameworks and reference implementations for enterprise GenAI
• Partner with client business and engineering teams and act as a trusted technical advisor to senior and executive stakeholders
• Integrate GenAI solutions with enterprise data platforms, APIs and external systems, optimizing latency, cost, accuracy, scalability, security and compliance
• Convert PoCs into production-ready platforms and architect scalable, cloud-native systems

Required profile

• 3-5 years of experience in software engineering, data engineering or AI/ML
• Proven delivery in client-facing, high-ambiguity environments
• Excellent communicator with technical teams and business stakeholders, with direct client exposure
• Preferred: prior Forward Deployed Engineer / Solutions Engineer / Field Engineer background, experience building 0 to 1 AI or data products, consulting/global delivery or field engineering experience

Required skills

• Expert Python for large-scale, high-performance applications
• Distributed systems, microservices, APIs and cloud-native architectures
• Preferred: LLM APIs, prompt engineering, RAG, vector databases, agentic workflows, MLOps and model monitoring
• Preferred: DevOps, CI/CD pipelines, infrastructure automation, frontend exposure for AI apps
• Preferred: hyperscaler ecosystems (Azure, AWS, GCP) and tools such as OpenAI, Claude, Cursor, Palantir