Machine Learning Architect, Data

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of the complete technical architecture for machine learning and AI initiatives, from design to production acceptance.

• Establish reference architectures for Fabric-first data science and MLOps platforms, outlining environment topology, storage boundaries, and promotion pathways.

• Generate architecture decision records, requirements traceability, and design documentation for security and compliance evaluations.

• Make decisions regarding platform trade-offs among Fabric, Azure-native services, managed/custom components, and various build/configure options.

• Determine compute sizing, cost guidelines, and capacity planning for both batch and inference workloads.

• Create governance patterns for third-party and open-source components.

• Design and architect sandbox, development/staging, and production environments with appropriate isolation and role-based access controls.

• Develop governed data access, data science write-back boundaries, and reusable batch prediction and forecasting pipelines.

• Construct CI/CD, promotion, orchestration, scheduling, data quality gates, versioning, observability, drift monitoring, backup, recovery, and retention architectures.

• Architect production generative AI and LLM solutions utilizing Azure OpenAI, retrieval-augmented generation, agents, evaluation, guardrails, and LLMOps practices.

• Design solutions that integrate Microsoft Fabric with Azure Machine Learning, Azure OpenAI, Azure AI Search, Databricks, Data Factory, Cosmos DB, and Azure Storage.

• Define architecture for identity, networking, security, compliance, performance, reliability, and observability.

• Act as a senior technical voice during client design sessions and workshops.

• Provide guidance to clients on AI and data platform roadmaps, selection of platforms, and sequencing of investments.

• Lead knowledge transfer and operational handover processes.

• Offer technical guidance, conduct design and code reviews, provide mentoring, create solution documentation, and develop reusable accelerators and internal reference architectures.

⛳️ Requirements

• Over 5 years of professional experience in data, machine learning, or AI engineering, with at least 3 years in solution architecture or lead technical design roles.

• Proven experience in overseeing monitored, scheduled production machine learning systems managed by individuals other than the author.

• Hands-on architectural experience with Microsoft Azure data and AI services, including Microsoft Fabric and lakehouse architectures.

• Experience in production generative AI, encompassing large language model solutions, retrieval-augmented generation, and prompt-based workflows beyond initial proofs of concept.

• Proficient in Python and SQL.

• In-depth expertise in MLOps, covering lifecycle management, versioning, reproducibility, evaluation, monitoring, drift detection, and retraining strategies.

• Experience with CI/CD and automation using Azure DevOps or GitHub Actions for data, notebooks, and model assets.

• Strong working knowledge of Entra ID, managed identities, service principals, Key Vault, and RBAC.

• Experience designing orchestration, scheduling, and data quality validation for production pipelines.

• Exceptional written and verbal communication skills suitable for both executive and technical audiences.

• Ability to manage technical scope, priorities, and expectations across multiple concurrent engagements.

• Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.

• Preferred: Experience with production-scale time-series forecasting; experimentation platforms; MLflow; R, renv, Great Expectations or Soda; Bicep or Terraform; Spark; Power BI and semantic modeling; Azure certifications; consulting/professional services; and work within regulated or security-reviewed environments.

🏝️ Benefits

• Strong focus on learning, innovation, and technical leadership.

• Collaborative, remote-first consulting culture with a team of experienced architects and practitioners.

• Direct involvement in the entire AI lifecycle, from strategy and design to production and optimization.

• Ownership of architecture in high-impact, real-world engagements across various industries.