Lead Forward Deployed Engineer - Databricks

About the role

Lead Forward Deployed Engineers (LFDE) at Deloitte help clients transform AI ambition into enterprise-scale impact by pairing leading-class engineering with pod-based delivery and vertical expertise.

Responsibilities

• Serve as the senior practitioner-leader embedded directly with our most 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-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.
• Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls.
• Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.
• Review and contribute to production-quality code.
• 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.
• 7+ 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 Databricks including hands-on experience with one of the following key platform technologies; DBRX, MLflow, Vector Search, Databricks AI Gateway.
• 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%, on average, based on the work you do and the clients and industries/sectors you serve.

Preferred Qualifications

• 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.