Customer Solutions Engineer

Build the Future Workforce

Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations.

Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.

Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.

Join Wand in leading the Agentic Shift

Wand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers.

You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern.

About the Role

We’re looking for a Field Solutions Engineer to sit at the intersection of engineering and go-to-market: pairing deep hands-on building with direct customer work to turn Wand’s platform into real agent-hours, workflows, and value in production.

You’ll work across the lifecycle from early technical validation and pilots through rollout and adoption translating messy business problems into concrete, testable workflows, building believable prototypes, and helping customers scale usage across many use cases.

If you love writing Python, wiring up APIs, and experimenting with LLMs as much as you enjoy talking to customers, this is a chance to shape how enterprises actually adopt the next generation of AI systems.

Responsibilities:

• Build and ship real solutions

  • Design and implement workflows, agents, and integrations on Wand for live customer scenarios (not just demo toys).
  • Turn one-off builds into reusable patterns: templates, starter kits, and internal tools other CEs/SEs can reuse.
  • Own technical quality: performance, reliability, and auditability of what you ship.

• Lead technical validation (“Proof-to-Contract”)

  • Help customers frame clear technical hypotheses: what needs to be proven (accuracy, coverage, latency, safety) for them to buy.
  • Design lightweight proofs, benchmarks, and evals that credibly answer, “Does this actually work for our data and process?”
  • Set up simple measurement for before/after: time saved, error reduction, or volume handled.

• Support pre-sales and pilots

  • Partner with Sales to shape and deliver believable demos and pilots that showcase Wand’s differentiators (agentic workflows, governance, complex document workflows, etc.).
  • Adapt and extend our demo environments rather than rebuilding from scratch each time.
  • Join customer calls to explain how things work under the hood and handle deep technical questions.

• Drive onboarding, adoption, and expansion

  • Help design and run onboarding plans: initial workflows, training sessions, office hours, and enablement for power users.
  • Instrument your solutions for usage and value (DAU/WAU, agent-hours, workflow runs) and use this data to guide iteration.
  • Partner with Customer Success to identify new use cases and turn proven workflows into broader rollouts.

• Handle issues with urgency

  • Debug and triage customer-facing issues, working closely with core Engineering when needed.
  • Provide clear, customer-ready explanations, workarounds, and follow-ups.
  • Feed recurring issues back into platform fixes, docs, and playbooks.

• Build the CE toolbox and playbooks

  • Contribute Python scripts, API workflows, and reference architectures that show “the Wand way” to build.
  • Document patterns for common use cases (e.g., contract review, research copilots, support augmentation) so the next deployment is faster.
  • Help define and refine internal CE rituals: intake criteria, pilot checklists, and demo/pilot templates.

Key Qualifications:

• 5+ years in a hands-on technical role such as Sales Engineer, Solutions Architect, Customer/Field Engineer, or Product-adjacent Engineer, working directly with enterprise customers.

Strong practical engineering skills:

• Comfortable writing Python in a professional context (scripts, services, ETL, automation).
• Experience integrating with APIs, webhooks, and data sources (files, DBs, SaaS tools).
• Able to design simple, robust systems and debug real-world failures.
• Experience building with LLMs or applied AI systems:
  • Prompting, RAG, or agent/workflow orchestration (homegrown or with tools like LangChain, LlamaIndex, etc.).
  • You’ve shipped at least one real application, prototype, or internal tool that used an LLM or similar model in production or a serious pilot.

Comfortable in front of customers:

• Can walk through an architecture diagram, show live workflows, and answer hard questions without hand-waving.
• Can listen deeply, translate fuzzy asks into concrete technical work, and push back when a request doesn’t make sense.
• Demonstrated ability to work across the lifecycle: discovery/solution design, proof/pilot build, rollout, and post-launch iteration.
• Bachelor’s or Master’s in Computer Science, Engineering, or equivalent practical experience.

Who You Are:

Builder First, Then Storyteller

You reach for a REPL or notebook before a slide. You’re happiest when you can show, not just tell — but you can narrate clearly for both engineers and executives.

Customer-Obsessed Problem Solver

You care less about “cool tech” and more about whether the workflow actually saves time, reduces risk, or lets the customer do something genuinely new.

Hands-On and Pragmatic

You’re comfortable with ambiguity and partial requirements. You find the smallest credible thing to build that proves the point and moves the deal or deployment forward.

Systems Thinker

You see how pre-sales, pilots, adoption, support, and metrics fit together and look for ways to turn one success into a pattern others can reuse.

Bias to Action and Ownership

You don’t wait for perfect specs. You pick up the ball, talk to whoever you need to, and get the workflow, demo, or fix over the line — then you document it so it’s easier next time.