Forward Deployed Engineer

Role: Forward Deployed Engineer

Location: Cincinnati, OH (local preferred; open to strong remote candidates)

Type: Contract through Feb 2027 with high likelihood of extension/conversion (7 months)

Work Authorization: W2 or C2C

Role Description:

Our client is seeking three Forward Deployed Engineers to support high-impact AI initiatives across their digital ecosystem.

These roles will align to one of two teams:

eCommerce Analytics AI Team: Focused on experimentation, personalization, and real-time decisioning platforms

AI Rapid Delivery Team: Focused on rapidly designing and deploying production-grade AI solutions across enterprise workflows

This role blends AI Solution Architecture %2B hands-on engineering, embedding directly with product, engineering, and business teams to deliver scalable, production-ready AI systems.

Key Responsibilities:

• Architect, design, and deploy AI-powered solutions across enterprise workflows and customer-facing platforms

• Build and implement LLM-enabled applications, including:

• Workflow automation and orchestration

• Agent-based systems and task execution

• Retrieval-Augmented Generation (RAG) solutions

• Develop systems supporting:

• Personalization and targeting

• Experimentation (A/B testing, multivariate, bandits)

• Real-time decisioning and digital experiences

• Integrate AI capabilities via APIs, microservices, and event-driven architectures

• Rapidly prototype and scale solutions from concept to production

• Partner with business stakeholders to translate ambiguous problems into deployable AI solutions

• Design human-in-the-loop workflows for high-impact use cases

• Implement observability for performance, latency, cost, and reliability tracking

• Build reusable frameworks and accelerators to scale AI delivery

• AI Validation & Evaluation

• Design evaluation frameworks for LLM, agent-based, and decisioning systems

• Assess model outputs, reasoning quality, and RAG performance

• Build automated pipelines for regression testing, prompt/model comparison, and validation

• Define and track metrics (accuracy, latency, cost, reliability, business impact)

• Support ongoing monitoring and optimization of production AI systems

Required Qualifications :

• 78%2B years of experience in software engineering, solution architecture, or AI/ML systems

• Proven experience building and deploying production-grade AI/LLM applications

• Hands-on experience with:

• LLM integration and prompt engineering

• API-driven architectures and microservices

• RAG systems and/or agent-based workflows

• Strong development background:

• Backend: Python and/or Node.js (FastAPI or similar)

• Frontend: React (integration-level experience)

• Experience working in cloud environments (Azure preferred)

• Strong understanding of scalable and distributed systems

• Ability to thrive in fast-paced, agile environments

Preferred Qualifications:

• Experience with experimentation platforms (A/B testing, personalization, optimization)

• Familiarity with LLM orchestration tools (LangChain, LangGraph)

• Experience with event-driven or asynchronous systems

• Exposure to AI observability (latency, cost, token usage tracking)

• Experience with cloud data platforms or large-scale data systems

• Understanding of AI governance, risk, and safety practices

• Platform & DevOps

• Experience with Docker, CI/CD pipelines

• Familiarity with Kubernetes or similar orchestration tools

• Experience building cloud-native applications

• Exposure to monitoring, logging, and distributed tracing

• Top 3 Skillsets

• Production AI/LLM application development

• Full-stack engineering (Python/Node %2B APIs, some frontend integration)

• Translating business problems into scalable AI solutions

• What Success Looks Like

• AI solutions move quickly from concept to production

• Systems are scalable, reliable, and measurable

• AI platforms deliver tangible business impact

• Experimentation and decisioning systems continuously improve

• Strong adoption of AI capabilities across teams

• Reusable frameworks accelerate delivery

• Why This Role Stands Out

• Opportunity to align with either a platform-focused or rapid delivery AI team

• Blend of architecture, engineering, and stakeholder engagement

• Focus on production AI systemsnot research

• Direct impact on real business outcomes across a large enterprise