Software Engineering Architect

Salesforce•San Francisco, CA

10d•$218,400 - $401,400

About The Position

Salesforce is seeking an Architect to set the strategy and technical direction for its Internal Agentic Platform, Model Context Protocol (MCP), LLM Model Gateway, and context engineering capabilities. This hands-on architecture role will shape how agents work, connect to tools and models, use enterprise knowledge, and support developers across the software development lifecycle. The role aims to help teams adopt AI safely and effectively, improve developer productivity, and avoid duplicating platform efforts. The Architect will define shared platform capabilities and standards across agent orchestration, integrations, runtimes, memory, model access, enterprise retrieval, security, governance, reliability, evaluation, and operations. They will help move AI agent projects from prototypes to production, advance AI-enabled developer experiences, and guide model selection and economics. The role also includes prototyping and validating designs, reviewing critical architecture and code, resolving technical tradeoffs, aligning partner teams, and mentoring engineers and technical leaders. A related technical degree is required, as is 15+ years of relevant experience.

Requirements

15+ years of experience designing and delivering large-scale distributed systems, developer platforms, AI platforms, or infrastructure services.
Track record of setting strategy and technical direction across teams and carrying it through implementation and production operation.
Strong expertise in agentic platform architecture and creating capabilities that make agent development and operation effective for application teams.
Hands-on experience with agentic frameworks such as Mastra AI and LangChain/LangGraph, including evaluating tradeoffs and establishing scalable platform patterns.
Experience designing model gateways, inference platforms, model-serving systems, model orchestration layers, or equivalent shared services.
Experience evaluating models against real workloads and understanding quality, latency, throughput, reliability, and cost tradeoffs.
Familiarity with SLMs, LLMs, model routing, fine-tuning, quantization, inference optimization, or other cost and performance improvements.
Experience building enterprise knowledge or retrieval systems using RAG, Graph RAG, search, or related approaches.
Experience designing APIs and extensible integration patterns.
Expertise in one or more relevant areas, such as identity and authorization, data platforms, retrieval and search, context management, workflow orchestration, platform security, or developer experience.
Understanding of software development workflows and how to assess AI’s impact on developer experience, productivity, and engineering outcomes.
Sound judgment regarding security, privacy, reliability, performance, and operational cost in fast-changing technical environments.
Strong communication skills and ability to build alignment across engineering, product, security, and business teams.
Genuine AI-first approach to engineering; experience using AI tools such as Claude Code, GitHub Copilot, Codex, or Cursor, and writing precise, structured prompts for reliable, secure, production-ready output.
A related technical degree is required.

Nice To Haves

Experience delivering production LLM or agent systems, including evaluation, telemetry, safety, and operational feedback loops.
Experience with coding assistants, AI-enabled developer tools, CI/CD workflows, automated testing, code review, or software engineering productivity initiatives.
Experience building internal platforms, paved paths, or developer tools adopted by multiple teams.
Experience with multi-model architectures spanning commercial APIs, open-weight models, and self-hosted or specialized models.
Contributions to technical standards, open-source projects, or cross-organization architecture initiatives.
Familiarity with MCP or comparable tool and protocol ecosystems.

Responsibilities

Own strategy and architecture for an agentic platform 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 capabilities for agent orchestration, tool and skill integration, runtimes, state and memory, human oversight, evaluation, and production operations.
Guide adoption and integration of agentic frameworks, including Mastra AI and LangChain/LangGraph.
Establish patterns for moving agent prototypes into dependable production systems, including customization, evaluation, observability, and ongoing improvement.
Drive AI-enabled developer experiences across the software development lifecycle, including code understanding and generation, testing, review, debugging, modernization, documentation, and release or operations workflows.
Define how MCP integrates with agent runtimes and the broader platform to support secure, governed, reusable connections to enterprise tools and systems.
Define the architecture and evolution of a secure, extensible LLM Model Gateway for consistent model access, policy enforcement, observability, resilience, and governance.
Develop a cost-effective model portfolio across SLMs and LLMs, evaluating 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 strategy for context engineering and enterprise knowledge management so agents can access relevant, grounded, permission-aware information.
Guide enterprise retrieval architecture, including RAG and Graph RAG, and its integration with enterprise data, knowledge sources, and agent workflows.
Set shared standards for security, privacy, governance, reliability, observability, evaluation, and human oversight across agents, models, tools, and data.
Prototype ideas, validate architecture through working systems, review critical designs, and help teams resolve technical tradeoffs.
Align engineering, product, security, data, and infrastructure partners around platform strategy, adoption, and delivery.
Communicate architectural decisions, influence across teams, and mentor engineers and technical leaders.
Design complex systems that integrate AI agents into human workflows; build and maintain shared system context; use AI in development; and review human- and AI-written code for correctness, quality, security, and performance.