Forward Deployed Engineer (FDE)

Role: Forward Deployed Engineer (FDE)
Location: Minneapolis, MN.
Duration: 6+ months

Role Summary

A Forward Deployed Engineer (FDE) is a hands-on engineer who works closely with client business and technology teams to design, build, and deploy AI-driven solutions that solve real, high-impact problems. This role blends strong software engineering fundamentals, GenAI expertise, and consulting skills to translate ambiguous requirements into production-ready solutions embedded directly into customer workflows.

FDEs operate at the intersection of engineering, AI, and customer delivery rapidly prototyping, deploying, and iterating solutions to drive measurable outcomes.

Key Responsibilities

1. Client-Embedded Problem Solving: Work directly with client stakeholders to understand workflows, pain points, and constraints, translating them into clear technical and AI use cases.
2. Solution Design & Development: Build end-to-end solutions using Python/Java, APIs, data pipelines, cloud services, and GenAI technologies (LLMs, prompts, agents).
3. Rapid Prototyping & Delivery: Develop and deploy prototypes and working solutions quickly, prioritizing speed-to-value and practical applicability over long development cycles.
4. Deployment & Integration: Integrate solutions into existing enterprise environments, systems, and workflows while ensuring reliability, security, and scalability.
5. Stakeholder Communication & Demos: Communicate complex technical solutions in clear business terms; support client demos, walkthroughs, and executive readouts.
6. Feedback & Continuous Improvement: Capture insights from client usage and feedback, refining solutions and sharing learnings with internal and client delivery teams.

Required Qualifications

1. Strong software engineering fundamentals, including data structures, system design, APIs, and cloud-based architectures.
2. Hands-on development experience with Python (or Java) building production-grade applications and services.
3. Hands-on experience applying AI Development Lifecycle (AIDLC) techniques to design and deliver AI solutions.
4. Practical experience with GenAI / LLMs, including prompt engineering and GenAI-powered applications.
5. Proven ability to work in ambiguous environments and deliver solutions end-to-end from problem definition through deployment.