AI Solutions Architect (Forward Deployed Engineer)

Location: Cincinnati, OH (Local Preferred; Open to Strong Remote Candidates)

Introduction: Our client is expanding its AI capabilities and seeking a Forward Deployed Engineer who combines the mindset of an AI Solutions Architect with the hands-on ability to design, build, and deploy production-grade AI systems. This role will align with one of two high-impact teams based on experience and business needs.

Responsibilities:

AI Solution Architecture & Delivery:

• Architect, design, and deploy AI-powered applications across enterprise and customer-facing platforms.
• Build and deliver production-grade solutions leveraging:
  • Large Language Models (LLMs)
  • Agent-based systems
  • Retrieval-Augmented Generation (RAG)
  • Workflow orchestration and automation
  • Decisioning and task-execution agents
• Rapidly prototype, validate, and scale AI solutions from concept through production.
• Embed AI capabilities into existing systems through APIs, microservices, and cloud-native architectures.
• Develop scalable AI services and integrations that support enterprise-wide adoption.
• Design human-in-the-loop workflows for high-impact AI use cases.
• Collaborate closely with cross-functional stakeholders to solve ambiguous business problems.

Personalization, Experimentation & Decisioning:

• Build systems that support:
  • Personalization and audience targeting
  • Experimentation platforms (A/B testing, multivariate testing, bandits)
  • Optimization engines
  • Real-time decisioning experiences
• Design scalable architectures that continuously improve decision quality and business outcomes.

AI Evaluation & Validation:

• Define and implement evaluation frameworks for:
  • LLM-based applications
  • Agent-driven workflows
  • RAG systems
  • Decisioning platforms
• Create automated pipelines for:
  • Regression testing
  • Prompt evaluation
  • Model comparisons
  • Continuous validation
• Measure and monitor:
  • Accuracy
  • Reliability
  • Latency
  • Cost
  • Business impact
• Ensure production AI systems remain scalable, trustworthy, and observable.

Platform & Engineering Excellence:

• Build reusable frameworks, templates, and accelerators that improve AI delivery speed.
• Implement monitoring, observability, and performance tracking.
• Build and maintain APIs and microservices that power AI-enabled capabilities.
• Support CI/CD pipelines and cloud-native deployment practices.
• Contribute to scalable, maintainable engineering patterns across the organization.

Requirements:

Required Qualifications:

• 7+ years of experience in Software Engineering, Solution Architecture, AI/ML Engineering, or related disciplines.
• Proven experience building and deploying production-grade AI applications.
• Hands-on experience integrating Large Language Models into real-world business solutions.
• Strong experience with:
  • Prompt engineering and evaluation
  • API-driven architectures
  • Microservices development
  • Distributed systems
• Experience designing and deploying:
  • Retrieval-Augmented Generation (RAG) solutions
  • Agent-based AI systems
  • Workflow orchestration platforms
• Strong backend engineering experience with:
  • Python (FastAPI or similar)
  • Node.js
• Experience integrating frontend applications (React preferred).
• Cloud platform experience, preferably Microsoft Azure.
• Ability to thrive in fast-paced, agile environments with evolving priorities.
• Strong communication skills and the ability to partner directly with business stakeholders.

Preferred Qualifications:

• Experience with experimentation and optimization platforms.
• Familiarity with:
  • A/B testing frameworks
  • Personalization platforms
  • Multi-armed bandit approaches
• Experience with LLM orchestration frameworks such as LangChain, LangGraph.
• Experience with event-driven and asynchronous architectures.
• Knowledge of AI observability, including:
  • Latency monitoring
  • Cost tracking
  • Token utilization analysis
• Familiarity with AI governance, safety, and risk management practices.
• Exposure to large-scale analytics and cloud data platforms.

Platform & DevOps Experience:

• Containerization using Docker.
• CI/CD pipeline implementation and automation.
• Kubernetes or similar orchestration platforms.
• Cloud-native application deployment.
• Monitoring, logging, and distributed tracing tools.

Team:

Team made up of AI Engineers, Data Scientists, Software Engineers, Product and Business Partners. Team culture: Ownership and accountability, Fast execution, Pragmatic problem-solving, Production-ready engineering, Measurable business impact.

Key Outcomes:

• AI solutions move from concept to production quickly and reliably.
• Systems are scalable, observable, and maintainable.
• AI-driven applications deliver measurable business outcomes.
• Experimentation and decisioning platforms continuously improve performance.
• Business teams successfully adopt and scale AI capabilities.
• Reusable frameworks accelerate AI innovation across the organization.

Why Join Us:

If you''''re an AI engineer who enjoys solving complex business challenges, building production-grade AI systems, and driving measurable outcomes, this is an opportunity to make a significant impact.

• Direct impact on enterprise-scale AI transformation initiatives.
• Opportunity to work across a broad range of AI use cases and business domains.
• Blend of architecture, engineering, product partnership, and innovation.
• Focus on delivering real-world AI solutions rather than research projects.
• High visibility and direct alignment to strategic business objectives.