AI Architect

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 #J-18808-Ljbffr Shrive Technologies LLC