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AI Architect

Shrive Technologies LLCUnited States🇺🇸United StatesPosted Oct 6, 2026

Why This Role Stands Out

As an AI Architect at Shrive Technologies, you'll drive innovation by designing and implementing cutting-edge enterprise-scale AI solutions, leveraging your deep expertise across leading AI platforms. This hybrid role offers significant growth potential for experienced AI professionals who thrive on strategic advisory and solution architecture, allowing you to shape the future of AI within a reputable company. You'll be instrumental in identifying new opportunities and building advanced AI systems, making this an exciting next step in your career.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
United States
Posted
23 hours ago
MLOpsNLPScikit-learnAzureDatabricksDeep LearningGDPRHugging FacePython

Job Description

Job Summary
Responsibilities
  • Architect Agentic AI solutions using Microsoft Foundry, Azure OpenAI, LangChain, LangGraph & multi-agent frameworks
  • Build AI solutions using frameworks such as Microsoft Agent Framework – Autogen, Semantic Kernel, Copilot Studio 
  • Well-versed with the Microsoft Agentic Framework (MAF)
  • Build RAG pipelines, vector DB integrations & autonomous workflow orchestration
  • Design and lead ML project lifecycles — data prep, modeling, training, evaluation, deployment & MLOps
  • Govern full SDLC for Data, ML, and GenAI platforms
  • Ensure strong security, compliance, governance (GDPR, CCPA, PII)
  • Produce robust architecture blueprints, ML design docs, and runbooks
  • Engage with customer IT and business leaders to understand pain points, priorities, success measures, and risks. 
  • Design secure, scalable data and AI solutions to deliver measurable business value.
  • Lead architecture design sessions, develop data/AI and analytics roadmaps to drive PoCs and MVPs. 
  • Accelerate adoption and ensure long-term technical viability.
  • Deliver Production-ready GenAI/Agentic applications.
  • Fine-tuned models and reproducible experiments.
  • Provide Clear documentation, test coverage, and deployment pipelines.
  • Regular updates on project status and deliverables to stakeholders.
  • Drive RFP/RFI solutioning, technical proposals, estimations & client workshops
Key Responsibilities
Responsibilities
  • Architect Agentic AI solutions using Microsoft Foundry, Azure OpenAI, LangChain, LangGraph & multi-agent frameworks
  • Build AI solutions using frameworks such as Microsoft Agent Framework – Autogen, Semantic Kernel, Copilot Studio 
  • Well-versed with the Microsoft Agentic Framework (MAF)
  • Build RAG pipelines, vector DB integrations & autonomous workflow orchestration
  • Design and lead ML project lifecycles — data prep, modeling, training, evaluation, deployment & MLOps
  • Govern full SDLC for Data, ML, and GenAI platforms
  • Ensure strong security, compliance, governance (GDPR, CCPA, PII)
  • Produce robust architecture blueprints, ML design docs, and runbooks
  • Engage with customer IT and business leaders to understand pain points, priorities, success measures, and risks. 
  • Design secure, scalable data and AI solutions to deliver measurable business value.
  • Lead architecture design sessions, develop data/AI and analytics roadmaps to drive PoCs and MVPs. 
  • Accelerate adoption and ensure long-term technical viability.
  • Deliver Production-ready GenAI/Agentic applications.
  • Fine-tuned models and reproducible experiments.
  • Provide Clear documentation, test coverage, and deployment pipelines.
  • Regular updates on project status and deliverables to stakeholders.
  • Drive RFP/RFI solutioning, technical proposals, estimations & client workshops
Skill Requirements
Skill & Experience
  • 15+ years in Data/AI/ML Engineering
  • Strong exposure to Microsoft Azure stack including Synapse, Fabric, Foundry, Copilot Studio, Azure App Insights
  • Hands-on with:
    • ML projects (supervised/unsupervised, forecasting, NLP, deep learning)
    • ML modeling tools: Python, PySpark, Azure ML, Databricks, Scikit-learn
    • Microsoft Foundry, Microsoft Agentic Framework
    • LLMs, embeddings, vector databases, RAG/GraphRAG, prompt optimization, and safety/guardrails
    • GenAI tools: MCP Server, Hugging Face Transformers, OpenAI APIs, and diffusion models (for image generation).
    • CI/CD, MLOps/LLMOps, SDLC
    • Explainable AI (XAI)
  • Cloud certifications  (Microsoft Azure) is a plus

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