Lead Forward Deployed Engineer - Databricks

Recruiting for this role ends on September 30, 2026.

About the role

As a Lead Forward Deployed Engineer (LFDE) at Deloitte, you'll help clients turn AI ambition into enterprise-scale impact by pairing leading-class engineering with pod-based delivery and vertical expertise. This role is ideal if you thrive at the intersection of product, engineering, problem-solving, and client impact, positioning you at the forefront of AI transformations.

As a Lead Databricks FDE, you will 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. You'll set technical direction, remove delivery blockers, and stay hands-on—designing, reviewing, and debugging systems with the team. You'll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level while maintaining hands-on technical credibility sets you apart.

Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale.

Responsibilities

Client Engagement

• Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders.
• Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk), and create a phased plan from prototype to production and scaling.
• Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision.
• Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements—contributing to pipeline development and deal shaping.

Cross-Functional Pod Leadership & Program Governance

• 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.
• Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience.
• Mentor and develop junior FDEs.

GenAI Solution Development

• Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms.
• Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls.
• Govern end-to-end RAG pipeline design—including ingestion, chunking, embedding, vector retrieval, and hybrid search—ensuring production-grade quality and scalability.
• Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards.

Engineering & Data Foundations

• 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.
• Demonstrate deep familiarity 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.

Limited immigration sponsorship may be available.

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 in 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.
• Experience operating within hybrid onshore/offshore teams.
• Familiarity with security, privacy, and compliance considerations.

Pay

The wage range for this role takes into account a wide range of factors including skill sets, experience, training, licensure, certifications, and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. A reasonable estimate of the current range is $189,200 to $372,900.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, where an award, if any, depends on various factors, including individual and organizational performance.