AI Architect@ REMOTE||Louisville - KY

AI Architect

Location:

REMOTE

Project duration: 1 year

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Skills Required:

• Large Language Models (LLMs)
• Agentic AI frameworks
• Multi-agent architectures
• RAG and Knowledge Systems
• Vector Databases
• Kubernetes and Cloud Platforms
• Python, Java, Type Script, or equivalent
• API and Microservices Architecture
• AI Evaluation and Benchmarking
• MLOps / LLMOps / Agent Ops
• Observability and Telemetry Platforms
• Security and Responsible AI Practices

Key Responsibilities:

• Enterprise AI & Agent Architecture
• Define enterprise reference architectures for AI Agents, Agent Factory, Agent Runtime, AI Control Plane, and Agent Ops platforms.
• Establish architecture standards, design patterns, reusable frameworks, and implementation guardrails.
• Lead technical strategy for Agentic AI adoption across SDLC and business domains.
• Drive architecture reviews and technical governance for AI initiatives.
• Create roadmap for enterprise-scale multi-agent ecosystems and agent interoperability.
• Agent Factory Leadership
• Own the technical vision for the Agent Factory platform.
• Define reusable agent frameworks, SDKs, templates, accelerators, and golden paths.
• Standardize agent lifecycle processes including Design → Build → Test → Publish → Run → Improve.
• Build reusable agent components such as:
• Memory services
• Context engineering
• Workflow orchestration
• Business rules engines
• Tool registries
• MCP integration patterns
• Agent-to-Agent communication frameworks
• Accelerate delivery through reusable agent assets and shared services
• AI Platform Engineering
• Architect and build enterprise AI platforms supporting developer productivity and self-service adoption.
• Design cloud-native AI infrastructure leveraging Kubernetes, AI runtimes, model serving, and orchestration frameworks.
• Establish platform engineering practices for scalability, resiliency, observability, and security.
• Partner with infrastructure teams to create enterprise-grade AI environments.
• AI for SDLC Enablement
• Lead development of AI agents supporting:
• Requirements engineering
• Architecture design
• Development
• Code review
• Testing
• Security validation
• Release management
• Production operations
• Establish AI evaluation frameworks and quality gates throughout the SDLC lifecycle.
• Ensure AI solutions improve developer productivity and software quality outcomes.
• Technical Leadership
• Serve as the highest-level technical advisor for AI Enablement.
• Mentor architects, principal engineers, senior engineers, and engineering teams.
• Lead proof-of-concepts, proof-of-technologies, and innovation programs.
• Drive enterprise adoption of emerging AI technologies and practices.
• Influence executive stakeholders on strategic AI investments.
• Responsible AI & Governance
• Partner with Enterprise Architecture, Security, Risk, and Compliance teams.
• Design governance controls and approval workflows for AI solutions.
• Establish AI evaluation, testing, monitoring, and operational standards.
• Ensure compliance with Responsible AI policies and enterprise security requirements.
• Agent Ops & Operational Excellence
• Define reliability, observability, and operational standards for AI agents.
• Create telemetry, monitoring, tracing, and optimization frameworks.
• Establish Fin Ops and cost governance practices for AI workloads.
• Lead production readiness reviews and operational maturity assessments.

Required Qualifications:

• Education:
  
  Bachelor's degree in Computer Science, Engineering, or related discipline. Master's degree preferred.
• Experience:
  
  12+ years of software engineering and architecture experience. 7+ years of cloud-native architecture and platform engineering. 5+ years building AI/ML, GenAI, or Agentic AI solutions. Experience leading enterprise-scale platform initiatives. Proven success influencing senior technical and business leaders.

Success Metrics (First 12 Months):

• Deliver enterprise Agent Factory platform.
• Enable 10+ reusable agent frameworks and templates.
• Achieve >70% reuse of common AI components.
• Reduce AI solution delivery time by 50%.
• Establish enterprise AI architecture standards and governance gates.
• Launch production-ready AI for SDLC capabilities.
• Create self-service onboarding experience for engineering teams.
• Implement Agent Ops platform with enterprise observability and compliance…