Job Title: Senior AI Engineer - Generative AI & Agentic Systems
Experience: 10+ Years
Location: Pittsburgh, PA Preferred, Dallas, TX or Cleveland, OH
Employment Type: Full-Time
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.
- 10+ years of software engineering and architecture experience.
- 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 Google Cloud Platform).
- 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.
