
AI Architect
TechDigital · Redmond, WA
Strong hiring record · median 7 days open
Hiring signal
Actively hiring
Posted 7 days ago · still listed on the employer's own board · How we score
All reasons
In its favour
- Posted 7 days ago
- Still listed on the employer's own board
Key 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 & 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
Skills
- Foundry
- Azure OpenAI
- LangChain
- LangGraph
- AutoGen
- Semantic Kernel
- Copilot Studio
- Retrieval-Augmented Generation
- Machine Learning
- MLOps
- Generative AI
- GDPR
- Azure
- Synapse
- NLP
- Deep Learning
- Python
- PySpark
- Azure ML
- Databricks
- scikit-learn
- LLM
- Embeddings
- Vector Databases
- Model Context Protocol
- Hugging Face Transformers
- OpenAI
- LLMOps
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