Forward Deployed Engineer
Posted 2 days ago
The role in short
Design, implement, and deploy custom multi-AI agent workflows and Python applications directly with customers to optimize financial operations.
About Qurrent
Qurrent is pioneering the future of work. Founded in 2023 and headquartered in San Francisco, we build fully managed, autonomous digital workforces that take ownership of complex financial operations end-to-end.
Our customers, including leading enterprises across finance services, professional services, technology, and property management, trust us to run critical workflows like invoice-to-cash, procure-to-pay, record-to-report, and FP&A.
Our mission is to evolve the nature of work and we're looking for people who want to be part of building something that's categorically different from anything else in the AI landscape.
Forward Deployed Engineer
We are seeking highly skilled and results-driven Forward Deployed Engineers to join our growing team. In this pivotal role, you will be instrumental in delivering custom AI-agent solutions to our customers. You will collaborate closely with customers to understand their specific needs and challenges, then design, implement, and deliver AI solutions that exceed expectations.
Responsibilities:
- Leverage cutting-edge LLMs to build reliable systems of multi-AI agent workflows in companies
- Lead the end-to-end lifecycle of AI solution delivery, from initial customer needs assessment to successful deployment and ongoing support.
- Write Python applications that will deliver on project requirements documents, ensuring all deliverables are met on time and within budget.
- Partner with customers to identify and re-engineer business processes for optimization through AI integration, leveraging Qurrent’s advanced Operating System.
- Utilize your expertise in AI tools and LLMs to create and develop bespoke AI solutions as part of Qurrent’s engineering team.
- Develop and present demos for presales meetings to effectively communicate the potential of Qurrent’s solutions.
- Convey customer insights, driving continuous improvement of Qurrent’s Operating System.
Qualifications:
- 6+ years of experience in building complex technical projects in Python, preferably within applied AI or related technology sectors.
- Technical and conversational fluency in AI, LLMs, Machine Learning and at least one of the major clouds (i.e. AWS, GCP, Azure)
- Strong understanding of AI concepts, particularly in the use of LLMs and other AI techniques including RAG, agentic workflows, fine tuning, or other LLM-based tools
- Proficiency in process mapping and process re-engineering, with a passion for optimizing and re-imagining workflows.
- Proficiency in full-stack software development, and/or willingness to learn new technologies and frameworks.
- Experience putting ML/AI into production, and ability to talk through best practices and pitfalls.
- Experience in creating effective demos and presentations for pre-sales engagements.
- Demonstrated track record of delivering projects that exceed customer expectations.
- Strong analytical and problem-solving skills, with a proactive and results-oriented approach
- Strong consultative and communication skills, and the ability to convey complex technical concepts to diverse stakeholders.
What We Offer:
- Innovation: Be at the forefront of the AI revolution, shaping the future of enterprise applications.
- Environment: A dynamic and collaborative work environment where your ideas are valued.
- Compensation: Competitive compensation package, with significant equity upside.
- Teamwork: The chance to work on challenging and rewarding projects with a talented and passionate team.
- Growth: Continuous learning and development opportunities within a supportive and established network.
Qurrent offers excellent benefits, competitive compensation packages and generous equity plans. The base salary pay range for this role is $150,000 - 220,000 USD. Qurrent is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws.
Prep for this interview
5 questions likely to come up at Qurrent, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Strong understanding of AI concepts, particularly in the use of LLMs and other AI techniques including RAG, agentic workflows, fine tuning, or other LLM-based tools”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Cloud, deployment and security
They wrote: “Technical and conversational fluency in AI, LLMs, Machine Learning and at least one of the major clouds (i.e. AWS, GCP, Azure)”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
- 3Discovery and scoping
They wrote: “Write Python applications that will deliver on project requirements documents, ensuring all deliverables are met on time and within budget.”
Tell me about a time the customer asked for one thing but needed another.
How to answer
Why they ask: Good FDEs spot when a request is a guess at a solution. They want proof you can redirect a customer without losing their trust.
- What they asked for, and the detail that made you doubt it
- How you tested the doubt (shadowed users, looked at logs, ran a quick prototype)
- How you made the case to the customer
- What shipped and what changed for them
Tip: Pick a story where you checked your hunch with data or users before pushing back.
- 4Integrations and APIs
They wrote: “Partner with customers to identify and re-engineer business processes for optimization through AI integration, leveraging Qurrent’s advanced Operating System.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 5Ownership and working with customers
They wrote: “Qurrent is pioneering the future of work. Founded in 2023 and headquartered in San Francisco, we build fully managed, autonomous digital workforces that take ownership of complex financial operations end-to-end.”
Tell me about something you shipped that broke in production.
How to answer
Why they ask: Everyone has broken production. They want to hear you own it and change something afterwards.
- What broke and who it affected
- How you found out and what you did in the first hour
- The root cause, said plainly
- What you changed so it can't happen the same way again
Tip: Pick a real one with a cost. Spend most of the answer on what you changed.
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