Software Engineering Architect

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. The Experience Salesforce is transforming the Developer Experience using AI - to change how software is designed, built, tested, secured, and operated. We're looking for an Architect to set the strategy and technical direction for our Internal Agentic Platform, Model Context Protocol (MCP), LLM Model Gateway, and Context Engineering capabilities. This is a hands-on architecture role for a technical leader who can define a compelling platform experience and help deliver it in production. You'll shape how agents work, how they connect to tools and models, how they use enterprise knowledge, and how they support developers across the software development lifecycle (SDLC). Your work will help teams adopt AI safely and effectively, improve developer productivity, and avoid solving the same platform problems independently. What You'll Actually Be Doing • Own the strategy and architecture for a powerful agentic platform experience that enables teams to build, compose, deploy, and operate agents across Salesforce. Set architecture and adoption direction for MCP, including server and client patterns, tool discovery, interoperability, identity, authorization, and lifecycle management. • Define reusable platform capabilities for agent orchestration, tool and skill integration, agent runtimes, state and memory, human oversight, evaluation, and production operations. • Set technical direction for agentic framework adoption, including the use of Mastra AI and LangChain/LangGraph. Guide framework integration and evolution while keeping the platform coherent, extensible, and adaptable to new approaches. • Establish patterns that help teams move from prototypes to dependable production agents, with clear paths for customization, evaluation, observability, and ongoing improvement. • Drive new AI-enabled developer experiences across the SDLC, including code understanding and generation, test creation, code review, debugging, modernization, documentation, and release or operational workflows. • Define how MCP fits into agent runtimes and the broader platform, enabling agents to connect to enterprise tools and systems through secure, governed, and reusable integrations. • Define the architecture and evolution of a secure, extensible LLM Model Gateway that provides consistent model access, policy enforcement, observability, resilience, and governance across providers and deployment types. • Drive a cost-effective model portfolio spanning small language models (SLMs) and large language models (LLMs). Evaluate when to use hosted, open-weight, fine-tuned, or specialized models for different workloads. • Develop ways to measure and improve model economics, including cost per successful task, token and context efficiency, retry rates, inference utilization, and the impact of model or routing changes. • Own the strategy for context engineering and enterprise knowledge management so agents can access relevant, grounded, and permission-a