| Job Description |
AI Ops Architect. This position will interface with key stakeholders and apply your technical proficiency across different stages of the Software Development Life Cycle including Requirements Elicitation, Application Architecture definition and Design; play an important role in creating the high level design artifacts; deliver high quality code deliverables for a module, lead validation for all types of testing and support activities related to implementation, transition and warranty; be part of a learning culture, where teamwork and collaboration are encouraged, excellence is rewarded, and diversity is respected and valued.
Required Qualifications:
Candidate must be located within commuting distance of Greenwood Village, CO or be willing to relocate to the area. This position may require travel to project locations.
Bachelor's degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
Hands-on experience with AWS services.
Working knowledge of AI/GenAI for autonomous networks.
Proficiency in AWS AI (SageMaker, Bedrock, Comprehend )
Strong knowledge of open-source MLOps technologies (embeddings, fault detection models), LangGraph, vLLM.
Hands-on experience with LangChain, LangFuse, Llama 3.2 LLM, and RAG architectures.
Experience in Agentic AI implementation and MCP, A2A.
Minimum 10+ years in enterprise cloud architecture, with at least 3+ years in AI Ops and GenAI solutions.
Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply.
Preferred Qualifications:
Architect AI Ops Solutions: Design and implement AI-driven operational frameworks for predictive analytics, anomaly detection, and automated remediation.
Cloud Architecture Leadership: Build enterprise-grade solutions leveraging AWS services.
Open-Source AI Frameworks: Implement MLOps pipelines using embeddings, fault detection models, LangGraph, and vLLM.
Advanced AI Development: Hands-on experience with LangChain, LangFuse, Llama 3.2 LLM, and RAG-based architectures.
Agentic AI & MCP: Drive implementation of Agentic AI systems and Model Context Protocol (MCP), A2A for intelligent orchestration.
Collaboration: Work closely with cross-functional teams including Cloud Engineering, Data Science, and DevOps to deliver end-to-end AI Ops solutions. |
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