Databricks Associate Forward Deployed Engineer

About the Role

Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.

Key Responsibilities

• Client Engagement:
• 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.
• 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.

Requirements & Tech Stack

• Databricks
• GenAI/LLM
• Testing
• CI/CD
• Logging
• Versioning
• Documentation
• Spark
• Airflow/dbt
• Cloud environments (AWS, Azure, and/or Google Cloud)
• MLOps/LLMOps

Qualifications

• Bachelor's degree (or equivalent) in Computer Science, Data Science, Engineering, or related field.
• Active US government security clearance (minimum Secret level).
• 2+ 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: Databricks features for data engineering, data science, and analytics including Lakeflow Connect, Agent Bricks, and Databricks Apps.
• 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 and CI/CD DevOps in Databricks.
• Ability to work independently and collaborate as part of a team.
• Effective written and verbal communication skills.
• Meticulous attention to detail and quality of work product.
• Ability to build and sustain professional relationships.
• Ability to lead projects or workstreams.
• Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment.
• Strong interpersonal skills and professional demeanor.
• Ability to meet deadlines.
• Ability to mentor and provide clear guidance to others.
• 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.

Preferred Qualifications

• Databricks certifications (e.g., Data Engineer Professional, Machine Learning Professional) are highly 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 in regulated enterprise environments.