Forward Deployment Engineer

Embed with customer teams to turn high-value AI workflow opportunities into adopted, measurable production systems.

Remote / Hybrid

Full-time

The Forward Deployment Engineer is a hands-on AI integrator who works close to customer teams, operators, and internal engineering to identify high-value workflow opportunities, build practical agentic AI solutions, and convert successful field experiments into reusable deployment patterns.

• Embed with customer teams to identify, prioritize, and accelerate high-value AI workflow opportunities
• Build and integrate agentic AI workflows across investigations, reporting, operational analysis, customer operations, and internal tooling
• Replace manually intensive workflows with scalable, reusable AI-enabled solutions
• Partner with product, AI engineering, security, and customer stakeholders to move from prototype to production
• Create reusable workflow patterns, templates, implementation notes, and lightweight operational playbooks
• Drive hands-on adoption and enable customer teams on approved AI tools and operating patterns
• Track adoption metrics, operational KPIs, hours saved, quality improvements, and failure modes
• Share field learnings with the broader engineering team so one deployment improves the next

• Experience building or integrating AI, automation, data, cloud, or operational workflow systems
• Strong systems thinking and ability to find leverage inside messy real-world processes
• Comfort working directly with customer teams, operators, and technical stakeholders
• Ability to prototype quickly while keeping security, governance, and reliability in view
• Production-oriented mindset across evaluation, monitoring, failure handling, and adoption
• Clear written communication and ability to turn field work into reusable playbooks
• Bonus:
  Experience with LLMs, RAG, agentic systems, AWS AI services, Python tooling, or operational analytics

Outcome-driven

Solutions that run in production

Agentic by design

Systems that reason and act safely

Enterprise-first

Security, governance, scale

Small teams, high ownership

Direct impact on outcomes

• Hands-on ownership of customer-facing AI deployments
• Exposure to real operational problems where AI needs to produce measurable outcomes
• Collaboration across product, AI engineering, security, delivery, and customer teams
• Flexible work setup where applicable

1

Context + fit

2

Field workflow discussion

3

Cross-functional interview

4

Final conversation

Alignment + offer

We value clear problem-solving and production readiness over theoretical perfection.