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-aware information.
Guide the architecture of enterprise retrieval systems, including retrieval-augmented generation (RAG) and Graph RAG, and determine how they work 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.
Stay close to implementation by prototyping ideas, validating architecture through working systems, reviewing critical designs, and helping teams resolve technical tradeoffs.
Align engineering, product, security, data, and infrastructure partners around platform strategy, adoption, and delivery.
Communicate architectural decisions clearly, influence across teams, and mentor engineers and technical leaders.
Design complex systems where AI agents fit smoothly into human workflows, and ship secure, high-quality software with AI as a core part of your own development workflow. Build and maintain the shared system context that lets AI work accurately, and review code, whether written by people or AI, for correctness, quality, security, and performance.
You're Our Person If...
You have 15+ years of experience designing and delivering large-scale distributed systems, developer platforms, AI platforms, or infrastructure services.
You have a track record of setting strategy and technical direction across teams, then carrying it through implementation and production operation.
You have strong expertise in agentic platform architecture and experience creating platform capabilities that make agent development and operation effective for application teams.
You have hands-on experience with agentic frameworks such as Mastra AI and LangChain/LangGraph, including the ability to evaluate their tradeoffs and establish scalable platform patterns.
You have experience designing model gateways, inference platforms, model-serving systems, model orchestration layers, or equivalent shared services.
You have experience evaluating models against real workloads, with a practical understanding of quality, latency, throughput, reliability, and cost tradeoffs.
You are familiar with SLMs, LLMs, model routing, fine-tuning, quantization, inference optimization, or other ways to improve cost and performance.
You have experience building enterprise knowledge or retrieval systems using RAG, Graph RAG, search, or related approaches.
You have experience designing application programming interfaces (APIs) and extensible integration patterns. Familiarity with MCP or comparable tool and protocol ecosystems is a plus.
You have 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.
You understand software development workflows and how to assess whether AI capabilities improve developer experience, productivity, and engineering outcomes.
You use sound judgment around security, privacy, reliability, performance, and operational cost in fast-changing technical environments.
You have strong communication skills and the ability to build alignment across engineering, product, security, and business teams.
You bring a genuine AI-first approach to engineering. You use AI tools such as Claude Code, GitHub Copilot, Codex, or Cursor to move faster and work across the stack, and you write precise, structured prompts that make AI output reliable, secure, and production-ready.
A related technical degree required.
Even Better If...
You have experience delivering production LLM or agent systems, including evaluation, telemetry, safety, and operational feedback loops.
You have experience with coding assistants, AI-enabled developer tools, continuous integration and delivery (CI/CD) workflows, automated testing, code review, or software engineering productivity initiatives.
You have experience building internal platforms, paved paths, or developer tools adopted by multiple teams.
You have experience with multi-model architectures spanning commercial APIs, open-weight models, and self-hosted or specialized models.
You have contributed to technical standards, open-source projects, or cross-organization architecture initiatives.
Unleash Your Potential
When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.
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Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.

The typical base salary range for this position is $218,400 - $365,200 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $263,200 - $401,400 annually.

The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.
