Sphera

Sphera

Senior Principal Architect, AI

US Remote · Principal

Sponsorship not specified$173k-$277kDetected 12 days ago
PythonCode ReviewDatabricksVector DatabasesAzurePlatform EngineeringMachine LearningData ScienceLLMsRAGAgentic AILangGraphAI OrchestrationA/B TestingCybersecurityComplianceLeadership

About the role

  • 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.

Responsibilities

  • 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.
  • 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.
  • 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.
  • 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.
  • Experience developing corporate AI governance frameworks - acceptable use policies, model approval processes, usage management, and audit readiness.

Requirements

  • 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 workflows, or multi-model orchestration pipelines.
  • Deep practical knowledge of LLMs, prompt engineering, RAG, vector stores, and orchestration frameworks such as LangChain, LangGraph, or Semantic Kernel.
  • Experience owning an AI delivery lifecycle or methodology - including standards definition, team onboarding, and process governance across concurrent projects.
  • Familiarity with agentic AI tooling including Claude Code and Claude Desktop with MCP servers, and structured artifact-driven delivery models.

Compensation

  • $173,000.00 - $277,000.00 + Eligible for Variable Compensation Plan Commensurate with relevant qualifications and experience

Benefits

  • Medical, Dental, and Vision Insurance
  • Health Savings Account
  • Flexible Spending Account
  • 401(k) Retirement Plan with Company Match
  • Life and Disability Insurance
  • Paid Time Off and Holidays
  • Flexible Working Schedule

Equal opportunity

  • Equal Opportunity Employer.
  • If you require a reasonable accommodation for a disability during the application or recruiting process, please email us at accommodations@sphera.com to make your request.

This listing is sourced directly from Sphera's careers page and normalized into a canonical job model.