Expert Product Architect AI/ML

At Ensono, our Purpose is to be a relentless ally, disrupting the status quo and unleashing our clients to Do Great Things!  We enable our clients to achieve key business outcomes that reshape how our world runs. As an expert technology adviser and managed service provider with cross-platform certifications, Ensono empowers our clients to keep up with continuous change and embrace innovation.

We can Do Great Things because we have great Associates. The Ensono Core Values unify our diverse talents and are woven into how we do business. These five traits are the key to achieving our purpose.

Honesty – Reliability – Curiosity – Collaboration - Passion

About the role and what you'll be doing: 

Ensono is building a new portfolio of Managed AI Services, and we need a Product Architect to design the technical foundation. This is a senior, hands-on role where you'll architect how our AI services are built, integrated, and delivered to enterprise clients.

You'll work at the intersection of cloud platforms, AI/ML technologies, and managed services operations—designing solutions that integrate third-party AI capabilities (Azure OpenAI, AWS Bedrock, etc.) into scalable, secure, enterprise-grade managed offerings. This isn't about building AI models from scratch; it's about architecting the infrastructure, integrations, and operational frameworks that allow Ensono to deliver AI services reliably to clients.

This is not a client-facing role, but at times you'll engage with customers to understand their needs, translate those into technical architectures, and collaborate closely with Security, the CTO office, and Operations to ensure our services are production-ready.

If you have deep experience in cloud architecture, MLOps, and the managed services business model—and you want to shape a new product line from the ground up—this is your role.

Technical Architecture (70%)

• Design the end-to-end technical architecture for Ensono's Managed AI Service portfolio, including infrastructure, integrations, and operational tooling
• Architect solutions that integrate third-party AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) into Ensono's managed services delivery model
• Define reference architectures for MLOps pipelines, including model deployment, monitoring, drift detection, and retraining orchestration
• Design AI-specific observability and monitoring frameworks that enable proactive managed services delivery
• Architect AI FinOps dashboards and cost governance tooling to provide clients visibility into AI spend (tokens, GPU, APIs)
• Work with security teams to define security and governance architectures for AI workloads, including prompt security, data loss prevention, and access control
• Evaluate and recommend build vs. buy decisions for platform components

Collaboration & Stakeholder Engagement (30%)

• Partner with Security to ensure AI architectures meet enterprise security and compliance requirements
• Work with the CTO office to align AI service architecture with Ensono's broader technology strategy
• Collaborate with Operations to ensure architectures are operationally supportable at scale
• Engage with Product Management to translate market requirements into technical designs
• Contribute to service definitions, SLAs, and operational runbooks

What You'll Deliver (First 6 Months)

 

• AI Service Reference Architecture: A comprehensive technical architecture covering infrastructure, MLOps, governance, security, and FinOps for the Managed AI Service portfolio
• Technology Stack Recommendations: Evaluated and documented recommendations for third-party tools and platforms
• Integration Patterns: Defined patterns for integrating Azure AI, AWS AI, and other non-cloud platforms into Ensono's delivery model
• Operational Blueprints: Architecture documentation that Operations can use to build and support services
• Client-Ready Collateral: Technical architecture artifacts suitable for client presentations and proposals

We want all new Associates to succeed in their roles at Ensono. That's why we've outlined the job requirements below. To be considered for this role, it's important that you meet all Required Qualifications. If you do not meet all of the Preferred Qualifications, we still encourage you to apply. 

Required Qualifications

• 10+ years of experience in technical architecture, cloud engineering, or platform engineering roles
• Managed services background: Deep understanding of how managed services are designed, delivered, and operated—you've built services that are run by operations teams, not just deployed and handed off. Previous experience at an MSP is a plus.
• Cloud architecture expertise: Strong experience with Azure and/or AWS, including compute, networking, security, and managed AI services (Azure OpenAI, Azure ML, AWS Bedrock, SageMaker)
• MLOps and AI infrastructure: Experience architecting ML pipelines, model serving infrastructure, and AI platform components
• Observability and monitoring: Experience designing monitoring, alerting, and operational visibility solutions
• Private/hybrid cloud: Experience with on-premises, hybrid, or private cloud architectures
• Client-facing experience: Comfortable presenting to technical and executive audiences, translating complex topics into clear recommendations

Preferred Experience

• Experience in regulated industries (financial services, healthcare, government)
• Background in FinOps or cloud cost management
• Experience designing multi-tenant service architectures
• Familiarity with Responsible AI frameworks and AI governance
• Category Technologies:
  • Cloud Platforms Azure (required), AWS (required), GCP (nice to have)
  • AI/ML Platforms Azure OpenAI, Azure ML, AWS Bedrock, SageMaker, Vertex AI
  • MLOps Tools MLflow, Kubeflow, Weights & Biases, Azure ML Pipelines, SageMaker Pipelines
  • LLM Orchestration LangChain, Semantic Kernel, LlamaIndex
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