Senior Forward Deployed Engineer
Uses Cursor, Claude, and Salesforce coding assistants (Vibes) in daily workflow and prototypes with LLM frameworks like LangChain — heavy use of AI-assisted coding and agentic workflows.
About the Role
Senior Forward Deployed Engineer at Salesforce responsible for designing, building, and deploying agentic AI solutions end-to-end inside enterprise customer environments. Own architecture to production, mentor engineers, resolve complex integration and deployment issues, and influence product roadmap through field experience.
Job Description
Role
Senior Forward Deployed Engineers are technical builders who design, build, and deploy complex agentic AI solutions directly inside enterprise customer environments. You will partner with a Deployment Strategist, lead technical delivery end-to-end, mentor other engineers, and set engineering standards for agentic AI deployments.
Key Responsibilities
- Own end-to-end build and deployment of agentic AI solutions, from architecture decisions through production handoff.
- Design and ship agentic systems on the Agentforce platform: agent logic, tool calls, multi-agent orchestration, deterministic guardrails, and enterprise integration patterns.
- Manage the data lifecycle for AI applications: model design, processing pipelines, and data readiness across Salesforce Data 360, Snowflake, Databricks, and customer platforms.
- Troubleshoot and resolve complex technical blockers (data integration, model deployment, orchestration, performance regressions).
- Build and maintain agent performance dashboards and customer KPI reporting to track deployment health and business outcomes.
- Rapidly develop proofs-of-concept and MVPs with sound engineering judgment about trade-offs.
- Codify reusable patterns, assets, and internal frameworks derived from customer work.
- Mentor other engineers through code reviews, pairing, and shared work; provide senior technical judgment on critical deployments.
- Surface field insights to Product and Engineering to influence the Agentforce roadmap.
- Travel approximately 25% of the time to work embedded with customer engineers.
Requirements
- 6+ years of software engineering or technical delivery experience with proven end-to-end ownership of scalable production systems in enterprise AI, cloud, or SaaS environments.
- Degree in Computer Science or a related field.
- Expert in at least one of: Python, JavaScript/TypeScript, Java, or Apex; conversant in the others.
- Experience integrating LLMs into production and familiarity with frameworks such as LangChain or LlamaIndex; applied prompt engineering and responsible AI practices in customer contexts.
- Deep experience in data modeling, processing, and analytics with proficiency across platforms like Salesforce Data 360, Snowflake, or Databricks.
- Strong Salesforce platform expertise: Agentforce, Apex, LWC, Flows, and Salesforce APIs.
- Demonstrated mentorship, clear communication with engineering peers and customer stakeholders, and an entrepreneurial, delivery-focused mindset.
Nice-to-Haves
- Salesforce certifications (Administrator, Platform Developer I/II, Agentforce Specialist, System Architect).
- Familiarity with DevOps/CI-CD practices, observability tooling, or data governance frameworks.
- Experience architecting multi-cloud, multi-system enterprise AI solutions for Global 500 customers.
- Track record of influencing product roadmaps through field engineering insights; open-source contributions, published technical writing, or conference presentations.
Tools & Technologies (mentioned)
Cursor, Claude, Salesforce coding products like Vibes, LangChain, LlamaIndex, Salesforce Data 360, Snowflake, Databricks, Agentforce, Apex, LWC, Flows, Salesforce APIs, Python, JavaScript/TypeScript, Java, LLMs.
Other Notes
Salesforce provides accommodations during the application and recruiting process. The role highlights use of embedded AI tools in the engineering workflow and expects the engineer to pilot emerging AI tools and models.