Architect - Machine Learning and Data

Coretek is seeking an Architect, Machine Learning and Data to lead the design of production machine learning and generative AI platforms on Microsoft Azure and Microsoft Fabric. This role sits at the point where data platform architecture, MLOps, and applied AI meet. You will define how client organizations move models out of notebooks and into governed, monitored, reproducible production operation, and how generative AI capabilities are architected to be secure, evaluable, and cost-controlled at enterprise scale.

You will own the technical architecture on client engagements end to end: target-state design, environment and identity models, pipeline and promotion patterns, observability standards, and the operational handoff that lets a client run the platform without you.

You will work alongside data scientists, data engineers, and delivery leadership, translating requirements into architecture decisions and then staying close enough to implementation to be accountable for the result.

This is a delivery architecture role. Depth of production experience matters more than breadth of exposure, and the expectation is that you have personally been responsible for systems that ran unattended and were handed to someone else to operate.

Key Responsibilities:

Solution Architecture and Technical Strategy

• Own end-to-end technical architecture for machine learning and AI engagements, from target-state design through production acceptance.
• Define reference architectures for Fabric-first data science and MLOps platforms, including environment topology, storage boundaries, and promotion paths.
• Produce architecture decision records, requirements traceability, and design documentation that hold up under client security and compliance review.
• Make and defend platform tradeoff decisions: Fabric versus Azure-native services, managed versus custom components, build versus configure.
• Define compute sizing assumptions, cost guardrails, and capacity planning for batch and inference workloads.
• Establish third-party and open-source governance patterns, including dependency disclosure, licensing implications, and controls that keep unapproved packages out of production.

ML Platform, MLOps, and Operationalization

• Architect Sandbox, Dev/Staging, and Production environment models with enforced isolation and role-based access aligned to Entra ID group structures.
• Design governed read access to enterprise data warehouse sources alongside controlled data science owned write-back boundaries for features, model metadata, artifact references, predictions, and experiment results.
• Define reusable batch prediction and forecasting pipeline architectures spanning ingestion, feature preparation, quality validation, model execution, output persistence, and alerting.
• Architect forecasting-specific patterns where they diverge from batch scoring, including time-series inputs, rolling forecasts, and horizon-based outputs.
• Design CI/CD and promotion architecture for notebooks and platform assets: Git integration, branching standards, automated testing, deployment pipelines, approval gates, and rollback paths.
• Define orchestration and scheduling patterns covering time-based, trigger-based, and manual execution with dependency-level failure visibility.
• Architect data quality gates that block downstream model execution on failure, covering schema validation, null and range thresholds, and distributional anomaly detection.
• Mandate and design headless execution: all scheduled and production workloads run under managed identities or service principals with secrets in Azure Key Vault, never under individual user credentials.
• Establish model, code, environment, and package versioning standards so any production run is traceable to a versioned combination of code, configuration, environment, and data reference.
• Design observability and drift monitoring architecture, including baseline statistics, health checks, alert thresholds, routing, and escalation paths.
• Provide backup, recovery, and retention architecture input for data science owned tables, model artifacts, and experiment metadata.
• Define foundational experimentation platform patterns for experiment configuration, metrics, treatment assignment, matched datasets, and results.

Generative AI and LLMOps Architecture

• Architect production generative AI solutions on Azure OpenAI, including retrieval-augmented generation, summarization, classification, extraction, and conversational patterns.
• Design retrieval architectures and select vector stores appropriate to scale and query profile, spanning Azure Database for PostgreSQL with pgvector, Azure AI Search for hybrid keyword and vector retrieval, and scale-out alternatives.
• Define chunking, embedding, indexing, and reranking strategies, and the evaluation approach that proves retrieval quality rather than assuming it.
• Architect agent and multi-agent solutions using frameworks such as Pydantic AI, Semantic Kernel, AutoGen, or the Microsoft Agent Framework, with clear tool boundaries and failure handling.
• Establish LLMOps practice: prompt and version management, automated evaluation harnesses, groundedness and hallucination testing, regression suites, and release gating.
• Design guardrails, content safety, and responsible AI controls, including PII handling, grounding constraints, and human-in-the-loop checkpoints where warranted.
• Define inference and orchestration patterns across API, serverless, and container-based deployment, with attention to latency, throughput, and failure modes.
• Architect token, cost, and model-selection strategies, including routing between model tiers and caching where it materially changes unit economics.
• Design observability for generative systems: tracing, evaluation telemetry, drift in output quality, and cost attribution.

Azure and Microsoft Fabric Platform

• Design solutions across Microsoft Fabric, including Lakehouse, Warehouse, Notebooks, Data Pipelines, deployment pipelines, and semantic models.
• Architect integrations across Azure Machine Learning, Azure OpenAI, Azure AI Search, Azure Databricks, Azure Data Factory, Cosmos DB, and Azure Storage.
• Define identity, networking, and security architecture including Entra ID, managed identities, service principals, Key Vault, RBAC, and private connectivity where required.
• Ensure downstream consumption patterns are validated, including Power BI access to model output and semantic layer design.
• Design for performance, reliability, security, compliance, and observability as first-class architectural concerns rather than post-deployment additions.

Client Engagement and Advisory

• Serve as the senior technical voice on engagements, leading design sessions and workshops with client architects, data science teams, and IT leadership.
• Communicate architecture, tradeoffs, risk, and cost to both engineering audiences and executive stakeholders, and drive consensus across them.
• Advise clients on AI and data platform roadmaps, platform selection, and sequencing of capability investment.
• Assess data readiness, AI maturity, and organizational constraints, and set realistic expectations about what production operation requires.
• Lead knowledge transfer and operational handoff so client teams can run, monitor, and troubleshoot what was delivered.

Technical Leadership and Collaboration

• Provide technical direction to consultants and engineers on engagement teams, including design review and code review.
• Mentor team members on Azure, Fabric, MLOps, and generative AI practice, raising the technical floor of the teams you work with.
• Author solution designs, runbooks, and reusable accelerators that outlive a single engagement.
• Contribute to internal reference architectures and delivery standards.
• Foster a collaborative, problem-solving culture across delivery teams.

Requirements

• 5+ years of professional experience in data, machine learning, or AI engineering, including 3+ years in a solution architecture or lead technical design capacity.
• Demonstrated ownership of production machine learning systems, meaning systems that executed on a schedule, were monitored, and were operated by someone other than the author.
• Hands-on architecture experience with Microsoft Azure data and AI services, including Microsoft Fabric and lakehouse architectures.
• Production generative AI experience, including large language model solutions, retrieval-augmented generation, and prompt-based workflows deployed beyond proof of concept.
• Strong Python and SQL, sufficient to review and correct the work of senior engineers.
• Deep MLOps expertise: model lifecycle management, versioning, reproducibility, evaluation, monitoring, drift detection, and retraining strategy.
• CI/CD and automation experience with Azure DevOps or GitHub Actions applied to data, notebook, and model assets.
• Working command of Azure identity and security: Entra ID, managed identities, service principals, Key Vault, and RBAC, including designing workloads that run without user-bound authentication.
• Experience architecting orchestration, scheduling, and data quality validation for production pipelines.
• Excellent written and verbal communication, with the ability to present architecture to executive stakeholders and defend it under technical challenge.
• Ability to manage technical scope, priorities, and expectations across concurrent engagements.
• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative discipline. Master's degree preferred.

Preferred Qualifications

• Experience with time-series forecasting at production scale, including rolling origin evaluation and horizon-based output design.
• Experimentation platform design: treatment assignment, matched datasets, causal inference methods, and result storage.
• Familiarity with MLflow, experiment tracking, and prompt and version management tooling.
• Working knowledge of R in a platform context, including renv and executing client-provided R workloads on a schedule.
• Experience with data quality frameworks such as Great Expectations or Soda.
• Infrastructure as Code with Bicep or Terraform.
• Spark-based processing in Databricks or Fabric.
• Power BI and semantic modeling depth.
• Azure certifications such as Azure Solutions Architect Expert (AZ-305), Fabric Data Engineer Associate (DP-700), Fabric Analytics Engineer Associate (DP-600), Azure Data Scientist Associate (DP-100), Azure AI Engineer Associate (AI-102), or Azure Data Engineer Associate (DP-203).
• Consulting or professional services background, delivering to fixed scope and milestone acceptance.
• Experience in regulated or security-reviewed environments where architecture is subject to formal review.

Why Join Coretek?

• Microsoft Azure Expert Partner delivering advanced AI, Generative AI, Data, and Fabric solutions.
• Ownership of architecture on high-impact, real-world engagements across multiple industries.
• Strong emphasis on learning, innovation, and technical leadership.
• Collaborative, remote-first consulting culture with experienced architects and practitioners.
• Direct exposure to the full AI lifecycle, from strategy and design through production and optimization.

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