Enterprise AI Architect

Enterprise AI Architect (2 positions)

The Enterprise AI Architect defines and governs the organizations enterprise-wide AI architecture, enabling secure, scalable, policy-driven, and cost-optimized Artificial Intelligence adoption. This role designs the architectural control layers governing model access, agent orchestration, routing logic, data boundaries, observability, and financial oversight across AI workloads. The Architect ensures that AI initiatives transition from experimentation into resilient, enterprise-grade production systems. The role operates at the intersection of enterprise architecture, Agentic AI systems, governance, cloud platforms, security, and FinOps.

Note: We are open to contract (full-time or part-time /20 hours a week) or Full-time / direct hire. Please send your resumes to jobs@SoothsayerAnalytics.com

Key Responsibilities

1. Enterprise AI Architecture Strategy

• Define and maintain the enterprise AI reference architecture across experience, control, agent, model, data, and observability layers.
• Translate executive AI strategy into scalable, vendor-neutral, modular technical blueprints.
• Ensure hybrid and multi-cloud compatibility aligned with enterprise standards.

2. Agentic AI Architecture

• Design and govern enterprise-grade multi-agent orchestration frameworks with controlled autonomy levels and policy-based guardrails.
• Establish agent lifecycle management including registration, monitoring, human-in-the-loop checkpoints, and retirement.
• Define clear architectural boundaries between agent-driven reasoning and authoritative enterprise systems of record.

3. Enterprise AI Platform Architecture and Model Orchestration

• Design centralized AI platform architecture governing model access, routing logic, guardrails, and access controls.
• Establish criteria-based model selection and orchestration based on risk, cost, latency, compliance, and performance requirements.
• Ensure production-grade reliability, operational visibility, and controlled separation between enterprise AI systems and experimental environments.

4. FinOps and AI Cost Governance

• Establish AI financial governance frameworks including usage metering, cost attribution, and chargeback models.
• Architect cost-performance optimization strategies including intelligent model selection and token consumption controls.
• Partner with Finance and Cloud teams to align AI consumption with enterprise budgeting discipline.

5. LLMOps and MLOps

• Define operational standards for model lifecycle management including onboarding, evaluation, versioning, and retirement.
• Implement CI/CD, rollback, monitoring, drift detection, and reliability engineering patterns for AI systems.
• Establish performance benchmarks and service-level governance for AI workloads.

6. Data Architecture for AI

• Define integration patterns between AI services and structured and unstructured enterprise data sources.
• Establish governance standards for retrieval, embeddings, contextualization, and secure data access.
• Maintain clear data ownership boundaries aligned with privacy and compliance requirements.

7. Enterprise Integration

• Architect scalable integration patterns between AI systems and enterprise platforms such as ERP, CRM, and operational systems.
• Define reusable APIs and service contracts for AI consumption across business units.
• Ensure AI remains advisory where appropriate and governed in high-impact workflows.

8. Governance and Risk

• Establish risk classification frameworks and production controls for AI workloads.
• Embed Responsible AI principles including explainability, audit readiness, and compliance alignment.
• Partner with Security, Legal, and Risk functions to institutionalize AI governance standards.

Required Qualifications

• 12+ years of experience in enterprise architecture or distributed systems.
• 5+ years of experience designing and deploying AI or ML systems in production.
• Demonstrated experience with Large Language Models and advanced retrieval architectures.
• Proven experience designing Agentic AI systems or multi-agent orchestration frameworks.
• Experience implementing cloud cost governance or FinOps frameworks.
• Strong background in API architecture, distributed systems, and cloud-native design.

Technical Expertise

• Agentic AI and multi-agent orchestration
• LLM integration patterns including RAG and tool calling
• Enterprise AI platform and control plane architectures
• FinOps and cloud cost optimization strategies
• MLOps and LLMOps lifecycle management
• Vector databases and hybrid retrieval systems
• Observability frameworks and reliability engineering
• Identity, access management, and enterprise security
• Multi-cloud architecture

About Us

Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning & GenAI solutionsspanning predictive analytics, optimization, NLP, computer vision, and enterprise-scale AI platformsthat help leading enterprises forecast, automate, and gain a competitive edge.

Please send your resumes to jobs@SoothsayerAnalytics.com