Senior Databricks Forward Deployed Engineer - GPS

## Responsibilities - Work alongside senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. - Embed with clients to identify business needs and translate high-value GenAI use cases into solutions. - Partner with leaders, product owners, architects, and engineers to align priorities and delivery. - Lead working sessions to shape solutions and drive client outcomes. - Prototype and deliver working AI solutions using industry expertise and emerging capabilities. - Contribute independently within an FDE pod while mentoring newer team members. - Coach client teams and end users on platform capabilities and AI enablement, while building trusted relationships, managing expectations, and supporting long-term engagement success. - Drive end-to-end sales and delivery support by developing demos/POCs, contributing to proposals and orals, articulating business value, and documenting solutions for smooth client handoff and knowledge transfer. - Strengthen team and organizational impact by mentoring other FDEs through design/code reviews and feedback, while contributing reusable components to intellectual capital. ## Solution Engineering - Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms. - Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls. - Apply architecture decisions that balance quality, safety, latency, cost, and model risk. - Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation. - Design extensible functionality, support sprint sizing, and align solutions with senior team members. - Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations. ## The Team - Deloitte's Government & Public Services (GPS) practice serves federal, state, and local government clients as well as public higher education institutions. - The team provides solutions for designing, developing, and operating cutting-edge Data and AI platforms, products, insights, and services. - The offerings help clients innovate, enhance, and operate their data, AI, and analytics capabilities. ## 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. - 5+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments. - 5+ years of experience with Databricks including hands-on experience with one of the following key platform technologies: Databricks features including Lakeflow Connect, Lakebase, Agent Bricks, Model Serving, Genie, and Databricks Apps. - 5+ years of experience leading project workstreams/engagements and translating business problems into AI solutions. - 3+ years of experience building reliable, maintainable, and well-documented code and CI/CD DevOps in Databricks. - Ability to travel 50%, on average, 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. - Ability to obtain and maintain a US government security clearance. ## Nice to Have - Active US government security clearance. - Professional Databricks certifications are preferred. - 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. - Experience operating within hybrid onshore/offshore teams. - Familiarity with security, privacy, and compliance considerations.