Lead Forward Deployed Engineer

Role

What you will build

• Own end-to-end build and deployment of agentic AI solutions for strategic customers, from architecture decisions through production handoff.
• Design Agentforce agent logic, tool calls, multi-agent orchestration, deterministic guardrails, and enterprise integration patterns.
• Own data models, processing pipelines, and AI data readiness across Salesforce Data 360, Snowflake, Databricks, and customer platforms.
• Resolve data integration failures, model deployment issues, orchestration breakdowns, and performance regressions.
• Build agent performance dashboards and customer KPI reporting; develop proofs of concept and MVPs.
• Create reusable patterns, assets, frameworks, and playbooks from customer work.
• Mentor engineers through code reviews, pairing, and shared work; provide technical depth on critical or at-risk deployments.
• Share field insights with Product and Engineering and partner with Deployment Strategists to deliver customer solutions.

Qualifications

• At least 6 years of software engineering or technical delivery experience and end-to-end ownership of scalable production systems in enterprise AI, cloud, or SaaS.
• Computer Science degree or related field.
• Expertise in at least one of Python, JavaScript/TypeScript, Java, or Apex; conversant in the others.
• Production LLM integration, LangChain or LlamaIndex, prompt engineering, and responsible AI experience in customer contexts.
• Deep data modeling, processing, and analytics experience with Salesforce Data 360, Snowflake, or Databricks.
• Deep Salesforce expertise in Agentforce, Apex, LWC, Flows, and Salesforce APIs.
• Experience mentoring technical talent and creating reusable assets; clear communication with technical peers, customer architects, and executives.
• Business-level Arabic and English proficiency and ability to travel to customer sites as needed.

Preferred qualifications

• Salesforce Administrator, Platform Developer I/II, Agentforce Specialist, or System Architect certifications are nice-to-haves.
• DevOps/CI-CD practices, observability tooling, and data governance frameworks are listed as nice-to-haves.
• The source’s final nice-to-have begins with experience architecting multi-agent systems but is truncated.