Lead Forward Deployed Engineer - Databricks

Recruiting for this role ends on September 30, 2026.

About the role

At Deloitte, Lead Forward Deployed Engineers (LFDE) don't just build AI solutions—they help clients turn AI ambition into enterprise-scale impact, pairing leading-class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.

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 by designing, reviewing, and debugging systems with the team.
• Translate engineering trade-offs into clear decisions for client leaders when needed.
• Build 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 stakeholders 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.
• Lead FDE pods of 2-5 onshore and offshore-supported engineers, owning execution, resource management, escalations, and overall delivery health.
• Enforce delivery standards across the pod, including 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.
• Architect and oversee delivery of LLM-enabled applications, including copilots, agentic workflows, assistants, and knowledge search experiences using 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.
• 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.
• Maintain 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: DBRX, MLflow, Vector Search, or 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 client needs.
• 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 factors such as skill sets, experience, training, licensure, certifications, and business needs. The disclosed range estimate has not been adjusted for geographic differentials. 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 program rules, where awards depend on individual and organizational performance.