Senior Principal Architect, AI

Sphera is a leading global provider of enterprise software and services that enables companies to manage and optimize their environmental, health, safety and sustainability. Our mission is to create a safer, more sustainable and productive world.

Sphera is a portfolio company of Blackstone, a U.S.-based alternative asset investment company that focuses on private equity, technology and innovation, and more. Blackstone businesses succeed through strong partnerships, a personalized approach and a commitment to exceptional performance with uncompromising integrity. Sphera and Blackstone are leaders in the Environmental, Social and Governance (ESG) space.

We are guided by our core values of Customer Centricity, Accountability, Bias to Action, Innovation, and Collaboration. These values help us recruit the right talent to join our rapidly expanding team around the globe. It is important to us that each and every Spherion is not only eager to challenge themselves and knows how to get work done but is an awesome addition to our company culture.

Sphera is seeking a Sr. Principal Architect, Artificial Intelligence to serve as the technical backbone of the AI Center of Excellence (COE). This is a hands-on practitioner role — equal parts architect, process owner, and governance lead — responsible for how AI is designed, delivered, governed, and scaled across the organization.

Key Responsibilities

1. AI Development Lifecycle (AIDLC)

Own and operate the AIDLC — Sphera's agentic software delivery framework that applies across all engineering, not just AI projects. In this model, AI agents are the primary execution mechanism: agents write code, generate tests, and validate outputs. Human roles shift to intent-setting, specification, oversight, and governance.

• Own the AIDLC as the standard delivery methodology across all software projects — governing how work moves from roadmap item through intent, specification and definition of ready, agentic code execution, multi-layer validation, and knowledge promotion back into the governance layer.
• Steward the full AIDLC toolkit — including the MCP server infrastructure, governance/spec/code repo structure, Claude Desktop and Claude Code configurations, role-based permission model, skills library, and structured artifact standards — maintaining, versioning, and evolving them as the framework matures.
• Establish and enforce the defined delivery roles across squads — ensuring each role operates within its scope and that agentic execution is properly supervised and validated at every stage.
• Drive adoption of the AIDLC operating model across engineering teams — onboarding squads, enforcing framework discipline, and intervening where teams drift toward ad-hoc approaches or accumulate governance gaps.
• Continuously improve the framework — capturing lessons from live delivery cycles, promoting reusable patterns into the governance layer, and evolving the toolkit as new tooling, model capabilities, and organizational learnings emerge.
• Leverage 3rd party standards and frameworks to help fill gaps in AIDLC governance framework and capabilities.

2. Hands-On Architecture & Prototyping

Serve as the senior technical practitioner within the AI COE — personally designing, building, and guiding AI solutions from concept through production across the portfolio.

• Design end-to-end AI solution architecture across the SpheraCloud Platform and product portfolio — including LLM integration patterns, agentic workflows, RAG pipelines, semantic search, and data services on Azure AI Foundry and Databricks Mosaic AI.
• Personally build proof-of-concept implementations — writing code, configuring AI pipelines, and validating model outputs across use cases including AI agents, data mapping, materials search, and predictive analytics.
• Embed directly within AI COE delivery squads, providing architecture guidance, code reviews, and hands-on support across active projects.
• Evaluate and benchmark emerging AI tools, models, and frameworks through structured experimentation, producing clear technical recommendations from hands-on testing.
• Define and apply AI solution evaluation frameworks — covering prompt quality, model accuracy, latency, cost, and production reliability — and monitor deployed features for drift and degradation.

Qualifications & Experience

• Bachelor’s degree in computer science, Data Science, Engineering, or equivalent practical experience building production AI systems.
• 8+ years in software or platform engineering or solutions architecture, with a clear shift toward AI/ML implementation in recent roles.
• 3+ years of hands-on experience designing and delivering production AI solutions — LLM applications, RAG systems, agentic workf