DmcfCn yA Yc5NXe77 – Generative AI & Agentic Systems | 12+ years exp required — Cleveland, OH

Role Overview

We are seeking an experienced Senior AI Engineer to lead the design, development, and deployment of enterprise-grade Generative AI and Agentic AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), AI orchestration frameworks, production-grade deployments, model governance, and end-to-end software delivery. This role requires close collaboration with business stakeholders, engineering teams, and leadership to deliver secure, scalable, and compliant AI solutions.

Key Responsibilities

AI Architecture & Delivery

• Design, build, and deploy scalable Generative AI and Agentic AI solutions leveraging LLMs and foundation models.

• Architect AI-driven applications using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and similar agentic ecosystems.

• Define end-to-end solution architecture, including model selection, prompt engineering, retrieval systems, orchestration, APIs, and integrations.

• Lead technical discussions and translate business requirements into scalable AI solutions.

Stakeholder & Client Engagement

• Partner with internal stakeholders and business leaders to gather, refine, and prioritize requirements.

• Conduct technical consultations and workshops to align AI solutions with business objectives.

• Act as the primary liaison between business teams and engineering teams throughout the delivery lifecycle.

• Present architectural recommendations, implementation approaches, and risk mitigation strategies to leadership.

Model Governance, Risk & Compliance

• Establish AI governance frameworks covering model lifecycle management, explainability, monitoring, and auditability.

• Ensure compliance with enterprise security standards, responsible AI practices, privacy requirements, and applicable regulations.

• Drive implementation of guardrails, human-in-the-loop controls, model evaluation, and risk management frameworks.

• Develop processes for model validation, performance monitoring, and regulatory adherence.

Engineering Excellence & SDLC Enablement

• Implement AI-assisted SDLC practices that improve team productivity, code quality, testing, documentation, and deployment efficiency.

• Create reusable AI accelerators, frameworks, and automation tools to enhance engineering productivity.

• Drive best practices for CI/CD, MLOps, LLMOps, model versioning, infrastructure automation, and release governance.

• Mentor development teams on AI engineering standards and operational excellence.

Production Deployment & Operations

• Lead deployment of AI solutions into production environments and ensure reliability, scalability, and observability.

• Monitor model performance, accuracy, latency, cost optimization, and user adoption metrics.

• Provide production support, incident resolution, root cause analysis, and continuous improvement.

• Manage AI application lifecycle from deployment through ongoing maintenance and enhancements.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.

• 12+ years of overall IT experience, including software engineering and architecture.

• 3+ years of hands-on experience with Generative AI, LLMs, and Agentic AI frameworks.

• Strong experience with OpenAI, Anthropic, Gemini, Azure OpenAI, AWS Bedrock, or equivalent AI platforms.

• Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar frameworks.

• Strong programming expertise in Python, Java, or related languages.

• Experience implementing AI/ML solutions in enterprise production environments.

• Hands-on experience with cloud platforms (AWS, Azure, or GCP).

• Knowledge of MLOps/LLMOps practices, model monitoring, and governance frameworks.

• Proven experience leading cross-functional teams and managing stakeholder expectations.

Preferred Qualifications

• Experience building Retrieval-Augmented Generation (RAG) solutions.

• Knowledge of vector databases such as Pinecone, Weaviate, Chroma, FAISS, or Azure AI Search.

• Experience with Responsible AI frameworks and AI governance programs.

• Certifications in Azure AI, AWS Machine Learning, Google AI, or related technologies.

• Experience in highly regulated industries such as Financial Services, Healthcare, Insurance, or Life Sciences.

•