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AI Architect

Job in Bellevue, King County, Washington, 98009, USA
Listing for: Katalyst-Healthcares-
Full Time position
Listed on 2026-10-24
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), AI Business & Operations
Salary/Wage Range or Industry Benchmark: 180000 - 260000 USD Yearly USD 180000.00 260000.00 YEAR
Job Description & How to Apply Below
Key Responsibilities:
  • Design AI-powered assistants, copilots, and agents to improve knowledge discovery, troubleshooting, and engineering productivity.
  • Lead the development of AI products, accelerators, and proof-of-concepts (PoCs) addressing business and operational challenges.
  • Build intelligent solutions for incident triage, RCA, predictive insights, automated recommendations, and workflow orchestration.
  • Lead and mentor AI engineers, data scientists, data engineers, software engineers, and domain experts.
  • Integrate structured and unstructured data, telemetry, documentation, and operational knowledge into unified AI platforms.
  • Establish AI architecture, governance, security, observability, and responsible AI standards.
  • Support solution design, bids, proposals, client presentations, executive briefings, and AI transformation initiatives through technical leadership.
Required Skills and Expertise:
Telecom Network Expertise:
  • Strong understanding of RAN, Core, Transport, OSS/BSS, Service Assurance, Network Operations, and Engineering domains.
AI and Agentic AI Architecture:
  • Expertise in LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Vector Databases, Multi-Agent Systems, and AI orchestration frameworks.
Network Operations Intelligence:
  • Experience building AI solutions for incident management, root cause analysis (RCA), anomaly detection, predictive maintenance, and operational automation.
Data and Cloud Architecture:
  • Expertise in Azure, Fabric, Databricks, Snowflake, data engineering, MLOps, LLMOps, APIs, and microservices.
Knowledge Management Platforms:
  • Experience with semantic search, engineering copilots, enterprise knowledge repositories, and decision-support systems.
Enterprise Architecture and Governance:
  • Define architecture standards, integration patterns, security controls, and AI governance frameworks.
Leadership and Stakeholder Management:
  • Lead cross-functional teams, mentor engineers, and drive enterprise-wide AI adoption.
AI Product Innovation and Pre-Sales:
  • Develop AI accelerators and support solution design, proposals, executive engagements, and client workshops.
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