Architect end-to-end GenAI solutions, including model lifecycle management, evaluation, deployment, and monitoring
Partner with ML/AI leads and engineering teams to integrate LLMs, autonomous agents, and GenAI services into existing and new solution pipelines
Act as a technical advisor and reviewer, supporting architectural decisions, code reviews, and design trade-offs
Provide architectural guidance for production deployments using Azure-native services, Databricks, and Azure DevOps CI/CD pipelines, ensuring solutions align with enterprise standards
Review and advise on infrastructure approaches, including containerization, orchestration, and infrastructure-as-code to ensure scalability, security, and operational readiness
Collaborate with product owners, engineering, security, and data teams to design solutions that meet functional and non-functional requirements (performance, reliability, compliance, and cost)
Establish and enforce governance for model usage, prompt and tool management, safety controls, data handling, and lifecycle management
Design integrations with enterprise data platforms, knowledge sources, and APIs, ensuring appropriate access controls and auditability
Guide implementation teams through design reviews, technical decision-making, and delivery of best practices
Document architectural patterns, methodologies, and lessons learned; contribute to internal knowledge bases and Communities of Practice
Evaluate emerging GenAI technologies and recommend adoption strategies based on business value and risk
Stay current with emerging research, tools, and frameworks in Generative AI, Agentic AI, and multi-agent systems

Requirements

Strong background designing and delivering modern cloud solutions, APIs, and data/AI platforms
Demonstrated experience with GenAI solution patterns
Familiarity with security, privacy, and governance practices for enterprise AI solutions
Ability to communicate complex concepts clearly to technical and non-technical audiences, influencing stakeholders through architecture guidance
Experience partnering across disciplines in agile delivery environments
Understanding application deployment, hosting, and scaling using the Azure App Service platform within Azure environments
Master’s degree or higher degree in Computer Science, Engineering, or related field (or an equivalent combination of education and experience)
7+ years of professional experience in architecture, software engineering, data engineering, or AI/ML solution delivery, with recent hands-on GenAI solution experience
Experience deploying AI/GenAI solutions in Azure environments, including familiarity with Azure AI Foundry and Databricks Mosaic AI would be highly valued

Benefits

Regular full-time and part-time employees have access to medical, dental, and vision plans
wellness program
health saving accounts
flexible spending accounts
401(k) plan
employee stock purchase program
life and accidental death & dismemberment (AD&D) insurance
short-term/long-term disability plans
emergency travel benefits
tuition reimbursement
professional membership fee coverage
paid family leave
ten paid holidays in each calendar year
vacation accrual between 10 and 20 days per year
paid sick leave

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

GenAI solutionsmodel lifecycle managementLLMsautonomous agentsAzure-native servicesDatabricksCI/CD pipelinescontainerizationinfrastructure-as-codeAPI design

Soft Skills

technical advisorcommunicationinfluencing stakeholderscollaborationdesign reviewsdecision-makingdocumentationgovernance enforcementproblem-solvingagile delivery

Certifications

Master’s degree in Computer ScienceMaster’s degree in Engineeringrelated field degree