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.
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