[Hiring] Principal Agentic AI Architect @Cynet Systems

Role Description

The candidate will serve as an Agentic AI Architect, designing secure and scalable data and AI solutions. The role involves leading machine learning project lifecycles, building RAG pipelines, and implementing multi-agent frameworks to drive measurable business value.

• Architect Agentic AI solutions using Microsoft Foundry, Azure OpenAI, LangChain, LangGraph, and multi-agent frameworks.
• Utilize the Microsoft Agentic Framework (MAF) to drive AI orchestration.
• Build RAG pipelines, vector database integrations, and autonomous workflow orchestration.
• Design and lead machine learning project lifecycles, including data preparation, modeling, training, evaluation, deployment, and MLOps.
• Govern the full SDLC for Data, Machine Learning, and Generative AI platforms.
• Drive RFP/RFI solutioning, technical proposals, estimations, and client workshops.
• Ensure strong security, compliance, and governance adherence, including GDPR, CCPA, and PII.
• Produce robust architecture blueprints, machine learning design documents, and runbooks.
• Engage with 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 and develop data, AI, and analytics roadmaps to drive Proofs of Concept (PoCs) and Minimum Viable Products (MVPs).
• Accelerate platform adoption and ensure long-term technical viability.

Qualifications

• 15+ years of experience in Data, AI, or Machine Learning Engineering.
• Strong exposure to the Microsoft Azure stack, including Synapse, Fabric, Foundry, Copilot Studio, Databricks, and Power BI.
• Hands-on experience with ML projects, including supervised and unsupervised learning, forecasting, NLP, and deep learning.
• Proficiency with ML modeling tools such as Python, PySpark, Azure ML, Databricks, and Scikit-learn.
• Direct experience with Microsoft Foundry and Microsoft Agentic Framework.
• In-depth expertise with LLMs, embeddings, vector databases, and RAG/GraphRAG architecture.
• Strong experience with CI/CD, MLOps/LLMOps, and full SDLC practices.
• Familiarity with Explainable AI (XAI) concepts and implementation.

Company Description