Define the Enterprise AI Blueprint
Establish and evolve the company’s AI reference architecture
Set standards for how AI systems are built, deployed, governed, and monitored
Design AI service layers that support both real-time and batch decisioning
Ensure multi-region readiness including US, EU, and alignment with data residency requirements
Act as the lead architect for AI initiatives across business domains
Build Predictive & Real-Time Intelligence
Architect predictive maintenance and IoT-driven data intelligence & anomaly detection systems
Design event-driven AI workflows integrated into business operations
Ensure sensitive data is encrypted, masked, and processed with consent-aware controls
Embed explainability and monitoring into all deployed models
Lead the Shift to Agentic AI
Architect supervised AI agents capable of reasoning, invoking tools, and acting across systems
Define guardrails, identity boundaries, escalation controls, and traceability
Ensure AI agents operate efficiently, effectively, and safely across Salesforce, Brightree, Oracle, Five9, ServiceNow, and internal workflows
Establish testing, red-teaming, and evaluation standards for agent behavior
Evolving the Multi-Model Strategy
Define how we leverage and govern multiple foundation models (OpenAI, Anthropic, and others)
Design routing, fallback, and cost-aware inference strategies
Implement RAG architectures with controlled retrieval and knowledge governance
Introduce structured evaluation to measure hallucination, grounding, and reliability
Prevent vendors lock-in through modular architecture
Establish MLOps & LLMOps Discipline
Define lifecycle governance from experimentation to production
Implement model registry, CI/CD pipelines, monitoring, and rollback mechanisms
Standardize feature management and drift detection
Create traceable audit frameworks across AI systems
Enable AI for Business functions, Products, & Services
Enable AI-powered sales enablement, service automation, and customer intelligence
Partner with business and R&D teams to embed intelligence into workflows, products and services
Support secure data monetization initiatives and advanced analytics capabilities
Embed Governance & Risk Management
Align AI architecture with HIPAA, GDPR, EU AI Act considerations, and recognized AI risk frameworks
Partner with Security, Legal, and Compliance to ensure AI systems are defensible
Build cost governance mechanisms to ensure AI usage is economically sustainable.

Requirements

Bachelor’s degree in computer science, Data Science, AI, or Mathematics (required)
Master’s or Ph.D. in a relevant field is preferred
10+ years in enterprise architecture, distributed systems, or data platforms
5+ years delivering AI/ML systems in production environments
Proven experience designing MLOps and LLMOps architectures
Deep understanding of LLM systems, RAG patterns, and agentic AI frameworks
Hands on experience, Python, Prompt Engineering, Reinforcement Learning
Experience integrating AI across enterprise SaaS platforms
Strong background in cloud-native architecture (Azure preferred)
Familiarity with Snowflake, Dataiku, or similar data and AI platforms
Experience operating in regulated or compliance-driven environments (medical device or healthcare preferred)
Excellent collaboration skills, with the ability to work effectively with cross-functional teams, including product, engineering, and business stakeholders.
Clear and effective communication skills, capable of explaining complex AI concepts to non-technical audiences.

Benefits

health, dental, and vision insurance
401(k) plan plus employer contribution and match
generous paid leaves such as vacation and sick leave
paid volunteer time
highly competitive and company-sponsored benefits
wellbeing programs rooted in our strong culture of excellence.

Applicant Tracking System Keywords

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

Hard Skills & Tools

AI architectureMLOpsLLMOpspredictive maintenanceanomaly detectionevent-driven AI workflowsPythonPrompt EngineeringReinforcement Learningcloud-native architecture

Soft Skills

collaborationcommunicationleadershipcross-functional teamworkproblem-solvingstrategic thinkingadaptabilitycritical thinkingstakeholder engagementexplainability

Certifications

Bachelor’s degree in computer scienceBachelor’s degree in Data ScienceBachelor’s degree in AIBachelor’s degree in MathematicsMaster’s degree in relevant fieldPh.D. in relevant field