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.

Work you'll do

As an AWS Forward Deployed Engineer (FDE), you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. Additional responsibilities include:

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.

The Team

Deloitte's Government & Public Services (GPS) practice - our people, ideas, technology and outcomes - is designed for impact. Serving federal, state, & local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.

Our AI & Data offering provides a full spectrum of solutions for designing, developing, and operating cutting-edge Data and AI platforms, products, insights, and services. Our offerings help clients innovate, enhance and operate their data, AI, and analytics capabilities, ensuring they can mature and scale effectively with organizational intelligence programs and differentiated strategies to win in their chosen markets.

Qualifications

Required:

• Bachelor's degree (or equivalent) in Computer Science, Data Science, Engineering, or related field.
• 4+ 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 AWS including hands on experience with one of the following key platform technologies; Amazon Bedrock, Bedrock Agents, Knowledge Bases, Guardrails
• 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
• 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

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