We’re seeking an Azure ML/AI
Architect who can design, build, and ship production AI solutions on Azure—then
partner with customers and field teams to land them. You’ll lead end -to -end
model lifecycle on Azure Machine Learning and Azure AI Foundry (Azure AI
Studio), orchestrate robust MLOps pipelines, and translate business goals into
scalable architectures. You’re equally comfortable whiteboarding with
executives, pairing with engineers, and tuning latency/cost for real -world
workloads. Experience across other clouds (AWS, Google Cloud, Oracle) is a
plus—we meet customers where they are.
Responsibilities
- Architecture &
Delivery
- Own reference
architectures for classical ML and GenAI (RAG, fine -tuning, tool/use -case
orchestration) on Azure ML + Azure AI Foundry.
- Design secure, scalable
MLOps with AML v2 (pipelines, components), GitHub Actions/Azure DevOps,
model/feature registries, online/batch endpoints, and CI/CD.
- Build data/feature
pipelines using Fabric/Synapse/Databricks, Delta/Parquet, and govern with
Purview; integrate Key Vault, Private Link, VNets, Managed Identity.
- Productionize inference
on Managed Online/Batch Endpoints or AKS; implement monitoring (drift,
data quality, performance, cost) and A/B/Canary rollouts.
- GenAI & Apps
- Implement Azure
OpenAI / Azure AI model catalog patterns (Prompt Flow, safety
filters, content moderation, grounding with vector search).
- Deliver RAG systems
(Azure Cognitive Search or vector DBs), retrieval evaluators,
prompt/version management, and cost/latency optimization.
- Solution Engineering
- Lead discovery, write
Solution/Architecture Design Docs, demo/reference apps, and run customer
workshops/POVs.
- Partner with
Sales/Customer Success; create estimates, landing zones, and handoffs to
customer/managed services teams.
- Standards &
Governance
- Embed Responsible AI
practices (privacy, safety, fairness, transparency), threat modeling, and
compliance -by -design.