Senior Forward-Deployed AI Engineer for Enterprise

Apply; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Equity

401(k) match

Parental leave

Wellness stipend

Learning stipend

Remote & Office

Flexible PTO

Holidays + flex days

Team outings

Referral bonuses

Job summary

Handshake is seeking a Forward Deployed Engineer for Handshake AI Enterprise. You will embed directly inside enterprise environments, learn each customer's workflows, and translate that understanding into production-grade AI agents that measurably change how they operate.

This role owns the full stack: defining the solution, building and deploying agents, designing evals, and iterating until performance improves.

Qualifications

• 5-6+ years of engineering experience, full-stack with backend depth.
• Real-world AI production experience beyond prototyping.
• Strong knowledge of agent architectures and evals.
• Comfort working with enterprise customers in high-stakes settings.
• Excellent systems thinking and multitask problem solving.
• High ownership mindset focused on outcomes.
• Strong communication with senior stakeholders.

Responsibilities

• Embed with enterprise customers in deployment teams and understand workflows.
• Define AI-driven solutions based on real business needs.
• Build and deploy production-grade agents for defined use cases.
• Design and run evals to measure agent performance and iterate.
• Develop domain expertise in customer verticals and apply it to AI.
• Collaborate with Product and Platform engineers to ship cohesive solutions.
• Establish engineering best practices and reusable patterns.
• Surface deployment insights to inform platform roadmap.

Skills

Senior engineer

AI in production

Agent architectures

Enterprise tech

Systems thinking

Ownership mindset

Stakeholder comms

Job description

Handshake is seeking a Forward Deployed Engineer for Handshake AI Enterprise. You will embed directly inside enterprise environments, learn each customer's workflows, and translate that understanding into production-grade AI agents that measurably change how they operate.

This role owns the full stack: defining the solution, building and deploying agents, designing evals, and iterating until performance improves.