Lead Anthropic Forward Deployed Engineer - GPS

About the role

The Deloitte AI & Engineering team transforms technology platforms, drives innovation, and makes significant impacts on clients' success. You'll work with talented professionals to reengineer operations and processes critical to businesses, helping them improve financial performance, accelerate new digital ventures, and fuel growth through innovation.

Responsibilities

• Serve as the senior practitioner-leader embedded directly with strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production.
• Set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team.
• Translate engineering trade-offs into clear decisions for client leaders when needed.
• Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements.
• Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations, and overall delivery health.
• Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates.
• Cross-functional pod leadership and program governance: coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
• Mentor and develop junior FDEs.
• Architect and oversee delivery of LLM-enabled applications, including copilots, agentic workflows, assistants, and knowledge search experiences.
• Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.
• Guide architecture of data pipelines powering GenAI use cases.
• Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices.
• Deeply familiar with cloud environments (AWS, Azure, and/or Google Cloud).

Requirements

• Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
• 10+ years of experience in software engineering, data engineering, data science, or analytics engineering.
• 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments.
• 1+ years of experience with Anthropic including hands-on experience with one of the following key platform technologies; Claude API, Claude for Enterprise, tool use, extended thinking, Claude Code.
• 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions.
• 1+ years of experience building reliable, maintainable, and well-documented code.
• Ability to travel 50% based on the work you do and the clients and industries/sectors you serve.
• Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
• Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking).
• Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments.
• Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation.
• Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management.
• Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures.
• Familiarity with security, privacy, and compliance considerations.

Qualifications

• Preferred: Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking).
• Preferred: Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments.
• Preferred: Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation.
• Preferred: Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management.
• Preferred: Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures.
• Preferred: Familiarity with security, privacy, and compliance considerations.