Forward Deployed Engineer (FDE)

Forward Deployed Engineer (FDE)

Minneapolis, MN onsite

Pay Rate: $55-60/hr on W2

6+ months contract (ongoing)

Role Summary

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 & Change: Communicate complex technical solutions in clear business terms, support client demos, walkthroughs, and executive discussions.

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 and experience building production applications.

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.

Preferred Qualifications:

6. Experience with AI orchestration or agent frameworks (e.g., multi-agent systems, workflow-driven AI).

7. Prior experience in the healthcare domain, including exposure to EHRs, clinical workflows, or regulated environments (HIPAA/PHI).

8. Experience with workflow automation, dashboards, and enterprise integrations.