Enterprise Architect, Azure

Enterprise Architect (Azure)

Location: Dallas, TX

Note: This position is not eligible for Immigration Sponsorship at this time.

We are seeking an accomplished Enterprise Architect to define and lead enterprise-wide strategy and architecture for cloud, data, analytics, and agentic AI platforms. This leader will shape a scalable, secure, and governed ecosystem on Microsoft Azure and Databricks Lakehouse, enabling high-impact data products and AI-powered solutions. Experience in Healthcare, Pharma, or Life Sciences—especially within regulated environments—is a strong advantage.

• Own and evolve target-state architecture for Lakehouse, Data Products, AI/ML, and Agentic AI platforms, aligned to business priorities and operating model.
• Conduct current-state assessments, identify gaps, and deliver roadmaps, reference architectures, and migration plans (including modernization from legacy data warehouses and fragmented AI tooling).
• Establish architecture standards, patterns, guardrails, and governance processes to enable consistent, repeatable delivery at scale.
• Drive technology decisions with clear trade-offs across time-to-value, cost, security, scalability, and compliance.

• Define Lakehouse patterns leveraging Medallion architecture, Delta Lake, and Unity Catalog, enabling governed, high-quality data products.
• Architect enterprise-grade Azure foundations: landing zones, identity, network segmentation, private access patterns, key management, observability, resiliency, and cost controls.
• Guide multi-tenant / multi-workspace strategies, cross-domain data sharing, and platform reliability patterns.

• Establish enterprise patterns for turning documents, PDFs, emails, clinical notes, call transcripts, images/scan artifacts, logs, and web content into structured and governed datasets.
• Design architectures for:
  • Document ingestion and classification, OCR/extraction, entity recognition, summarization, and schema mapping
  • RAG-ready pipelines (chunking, embeddings, vector indexing) and “structured outputs” pipelines (entities/relations/metrics into curated tables)
  • Metadata enrichment, taxonomy/ontology alignment, and lineage tracking for extracted content
  • Define governance and quality controls for extracted data (accuracy thresholds, exception workflows, human-in-the-loop review, and audit trails).

• Architect agentic AI frameworks (planner–executor, tool use, multi-agent collaboration) integrated with enterprise APIs, workflows, and data platforms.
• Define LLMOps standards including evaluation, prompt/model/version management, safety guardrails, monitoring, and feedback loops.
• Ensure responsible AI: privacy protections, policy enforcement, explainability, and auditability; align with enterprise risk posture and regulatory constraints.

• Implement governance and access controls using Unity Catalog, including RBAC/ABAC, row/column-level security, secrets management, encryption, auditing, and retention.
• Establish enterprise practices for data contracts, lineage, data quality, and SLAs/SLOs (e.g., Great Expectations/Deequ patterns).
• Partner with Security, Privacy, and Compliance teams to ensure architectures meet applicable regulatory obligations.

• Lead architecture workshops and solutioning sessions with business and technology stakeholders to define prioritized use cases and measurable outcomes.
• Provide delivery leadership: estimates, risk management, dependency management, and executive-ready status reporting.
• Coach and mentor architects and engineering teams; create reusable templates, accelerators, and best practices.

• Establish CI/CD standards for notebooks, jobs, repos, and pipelines; define environment strategy and release trains.
• Drive IaC adoption (Terraform/Bicep) for repeatable infrastructure and policy-as-code.
• Operationalize ML lifecycle with MLflow, feature stores, training-serving consistency, and production monitoring.
• Define monitoring,…