Build and improve reusable GitHub Actions workflows, actions and repository templates that make everyday development tasks easier, and introduce AI‑assisted and agentic engineering workflows on GitHub.
Responsibilities
Build and improve reusable GitHub Actions workflows, actions and repository templates that make everyday development tasks easier.
Build and introduce AI‑assisted and agentic engineering workflows on GitHub, helping teams adopt these capabilities within agreed standards.
Work with Platforms to help build and maintain GitHub Actions runners, reusable actions and workflows.
Work with the Principal DevOps Engineer and engineering teams to improve how GitHub is used, identify automation opportunities and simplify development.
Keep workflows and integrations secure, reliable and maintainable, supporting updates and helping teams diagnose and resolve issues.
Build and improve reusable GitHub Actions workflows, actions and repository templates that make everyday development tasks easier; build and introduce AI‑assisted and agentic engineering workflows on GitHub; work with Platforms to help build and maintain GitHub Actions runners, reusable actions and workflows; work with the Principal DevOps Engineer and engineering teams to improve how GitHub is used, identify automation opportunities and simplify development; keep workflows and integrations secure, reliable and maintainable; share knowledge, documentation and practical examples so teams can adopt, maintain and extend GitHub workflows and agentic engineering capabilities.
Responsibilities
Build and improve reusable GitHub Actions workflows, actions and repository templates
Build and introduce AI‑assisted and agentic engineering workflows on GitHub
Work with Platforms to help build and maintain GitHub Actions runners, reusable actions and workflows
Work with the Principal DevOps Engineer and engineering teams to improve how GitHub is used, identify automation opportunities and simplify development
Keep workflows and integrations secure, reliable and maintainable, supporting updates and helping teams diagnose and resolve issues
As an AI / ML Engineer, a typical day involves designing and building advanced applications and systems that leverage artificial intelligence technologies. This role requires integrating cloud-based or on-premises pipelines to ensure solutions are production-ready and scalable. The engineer works on implementing generative artificial intelligence models as part of comprehensive solutions, which may also encompass deep learning techniques, neural network architectures, conversational agents, and image analysis.
Responsibilities
Design, develop, and deploy production-grade Multi-Agent Systems (MAS) and agentic workflows using Gemini Enterprise
Identify need for Pro Code Vs No Code design based on customer problem statement and data sources
Design Deploy and custom agents using the Agent Development Kit (ADK), and Agent Platform AI Reasoning Engine and integrate with Gemini Enterprise
Build native integrations across the Google Workspace Ecosystem (Docs, Drive, Gmail, Calendar, Meet) and NotebookLM for grounded enterprise research, document automation, and conversational discovery
Architect scalable short-term session storage, conversation state persistence, and long-term memory architectures across multi-agent interactions
Design, develop, enhance, and support modular Python services and REST APIs for the EDGE V4 Calculation Engine.
Responsibilities
Design, develop, enhance, and support modular Python services and REST APIs for the EDGE V4 Calculation Engine.
Translate validated building science formulas, rules, assumptions, and dependency flows into accurate, maintainable, and testable Python components.
Develop and integrate calculation capabilities for energy, water, materials, embodied carbon, baseline standards, scenario simulation, explainability, anomaly management, and cost-related functions as prioritized by the product backlog.
Build reusable calculation libraries, data models, validation rules, configuration services, and version-controlled datasets using Python, FastAPI, Pydantic, SQL, and PostgreSQL or approved equivalent technologies.
Integrate calculation services with the EDGE V4 application through well-defined API contracts, structured inputs and outputs, secure authentication, and reliable error handling.
Are you passionate about cloud platforms, intuitive developer experiences, and building tools that empower developers? Do you want to shape how developers and AI agents build on Azure? Are you excited about creating the libraries, tools, and agent experiences that make Azure easier to discover, understand, and use?
Compensation
Software Engineering IC3 - The typical base pay range for this role across the U.S. is USD $102,100 - $202,200 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800 - $219,200 per year.
Responsibilities
Build and evolve tools and infrastructure for authoring, generating, validating, and maintaining high-quality Azure SDKs and client libraries.
Help shape how developers and AI agents discover, understand, and interact with Azure through experiences such as Azure MCP, Azure Skills, and Azure Developer CLI.
Design intuitive, consistent API and developer experiences across Azure SDK language ecosystems.
Apply AI and agentic technologies to improve SDK authoring, validation, developer workflows, and the overall experience of building on Azure.
Collaborate with Azure service teams to understand their needs and make it easier for them to deliver great developer and agent experiences.
Requirements
Bachelor's Degree in Computer Science or related technical field
Lead strategic AI partnerships to build and scale Salesforce’s Agentforce ecosystem, driving product, strategy, and GTM initiatives.
Compensation
The typical base salary range for this position is $197,300 - $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 - $344,700 annually.
Responsibilities
Build and lead strategic AI partnerships
Launch net‑new integration patterns and interop standards
Lead end‑to‑end execution of Web IQ developer experience and customer platform programs, coordinating across product, engineering, Azure, and business stakeholders.
Compensation
The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
Own integrated planning and execution for Web IQ developer experience programs
Translate product and customer priorities into a prioritized engineering backlog, milestones, owners, dependencies, and measurable exit criteria
Establish an operating rhythm for planning, delivery reviews, issue triage, launch readiness, and executive reporting
Coordinate across AISP engineering, Web IQ product, Azure, partner integrations, customer support, and go‑to‑market teams to deliver against shared commitments
Drive delivery of a streamlined end‑to‑end developer journey across Web IQ portal and Azure
Requirements
Bachelor's Degree
Nice to have: 12+ years experience engineering, product/technical program management, data analysis, or product development
Nice to have: 4+ years leading complex, cross‑functional technical programs
Nice to have: 8+ years driving large‑scale, multi‑team programs
Nice to have: Experience with developer portals, API onboarding, identity/authentication, Azure resource provisioning, usage metering, billing, or cloud marketplace integrations
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