AI Architect

Kforce's client is seeking an AI Architect - Agentic Platforms to define the architectural foundations that power the client's enterprise agent ecosystem. This role is responsible for designing and governing the architecture for agent-based integrations, agent registries, scoring/evals infrastructure, grounding patterns, and multi-agent orchestration platforms. The AI Architect provides deep technical leadership across engineering, product, data science, security, and cloud teams to ensure that agents are built safely, consistently, and with enterprise-grade reliability, performance, and observability. Responsibilities:

• Define and maintain the enterprise reference architecture for agentic platforms (agentic framework, tools, MCP, registries, evals, orchestration, grounding, observability)
• Establish architectural standards and best practices for agent design, tool integration, safety, telemetry, versioning, and lifecycle management
• Provide architectural leadership for agentic platform engineering teams, ensuring scalability, resiliency, performance, and operability
• Design and guide integration with semantic layers, embeddings, vector search, knowledge models, and enterprise data products to enable grounded agent behavior
• Drive architectural direction for low-code/no-code agent-building platforms, ensuring governance, consistency, and ease of adoption
• Partner with cloud, security, product, and enterprise architecture teams to align agentic platform designs with client's AI governance and RAI principles
• Define and support agentic SDLC through patterns for evals, safety tests, regression gates, monitoring, and benchmarking
• Evaluate new agentic frameworks, open-source standards, and orchestration tools to guide build vs buy platform decisions
• Provide hands-on architectural guidance across engineering and data science teams, enabling scalable, secure, and cost-efficient agent deployment
• Designing enterprise AI/ML systems; 1+ years hands-on with GenAI, agentic workflows, RAG, LLM-based integrations, or multi-agent systems
• Experience in cloud and distributed systems architecture focused on scalability, reliability, observability, and performance
• Strong expertise with agentic frameworks and tooling (MCP, LangChain, LangGraph,LlamaIndex, autogen, crewai, Agent sdk,OpenAI SDK etc)
• Hands-on experience in modern software development and engineering practices
• Proven experience integrating APIs and enterprise systems into agentic platforms and workflows
• Ability to rapidly build AI-driven prototypes, proofs of concept, and demo-ready product experiences
• Experience defining and governing enterprise architecture standards, patterns, and reference architectures
• Deep understanding of MCP servers, tool calling, registries, eval pipelines, agent observability, and multi-agent orchestration
• Hands-on experience with Azure and GCP, including Kubernetes, containerization, identity, networking, CI/CD, and API platforms
• Familiarity with AIOps/MLOps stacks (MLflow, model registries, vector DBs, semantic layers, feature stores, monitoring)
• Strong knowledge of security, compliance, risk, and Responsible AI (RAI) considerations for enterprise agent systems
• Demonstrated ability to partner across engineering, data science, product, and security teams to deliver complex AI platform architectures