Senior AWS Forward Deployed Engineer - GPS

About the role

The Deloitte AI & Engineering team transforms technology platforms, drives innovation, and makes a significant impact on clients' success. You'll work alongside talented professionals reimagining and reengineering operations and processes critical to businesses.

Responsibilities

• 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.
• 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.
• 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

• 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 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.

Qualifications

• Required: 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 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.

Pay

Reasonable estimate of the current range: $155,600 to $306,800.

Schedule

N/A