Job Details
Job Overview
Responsibilities
As a Forward Deployed Engineer, you’ll play a critical role in bringing advanced AI systems to life for customers. This is a deeply hands-on and highly collaborative role — you’ll work directly with client teams to understand their workflows, design solutions, and deploy production-grade AI systems in real environments. Your work will bridge technical execution with thoughtful communication and fast iteration, ensuring each deployment drives meaningful product impact.
- Partner directly with customers to understand their use cases, identify pain points, and translate needs into actionable technical solutions.
- Design and implement agentic workflows powered by LLMs for real-world task execution.
- Build customization layers tailored to organization-specific logic and deployment requirements.
- Deploy and iterate on LLM-powered pipelines with evaluation and feedback loops in production environments.
- Develop voice-based and message-based agents that drive engagement and adoption.
- Architect AI pipelines (RAG, prompt engineering, evaluation) and ensure reliable integration into complex infrastructures.
- Collaborate closely with product and engineering teams to bring field feedback directly into the product roadmap.
- Communicate clearly with both technical and non-technical stakeholders to align on goals and deliver solutions quickly.
Qualifications
Machine Learning Expertise
- 5+ years of experience in ML (PyTorch, TensorFlow).
- 2+ years of hands-on experience working with LLMs (Hugging Face, OpenAI, Anthropic).
AI System Development
- Proven experience building and deploying production AI systems, including RAG and vector search.
- Strong knowledge of prompt engineering, AI safety, and content filtering best practices.
- Comfort architecting scalable infrastructure that integrates into complex environments.
Customer Engagement & Communication
- Experience working directly with customers to deploy and iterate on technical solutions in real environments.
- Excellent communication skills with the ability to explain complex concepts clearly to both technical and non-technical stakeholders.
- Proven ability to gather feedback, shape product direction, and collaborate effectively with cross-functional teams.
Technical Proficiency
- Familiarity with Rails is a plus, but not required — strong candidates can ramp up quickly.
- Experience with REST APIs, PostgreSQL, ActiveRecord, and RSpec.
- Understanding of frameworks like LangChain or LlamaIndex, or the ability to learn them rapidly.
Builder Mindset
- Thrives in ambiguity, learns quickly, and iterates fast in lean environments.
- Excited to work in a small, high-impact team where communication and ownership are key.
Ideal Candidate
Ideal Candidate Profile
- Innovative Builder — Designs and deploys sophisticated AI workflows that solve complex, real-world enterprise challenges.
- Forward Deployed Mindset — Thrives on working directly with customers, gathering feedback, and iterating rapidly to solve problems in the field.
- Product-Minded Communicator — Excels at collaborating with users, clients, and cross-functional teams to shape solutions and drive adoption.
- Technically Excellent — Brings strong expertise in Rails (REST APIs, PostgreSQL, ActiveRecord, RSpec) and hands-on experience deploying scalable AI systems using modern frameworks and infrastructure.
- Collaborative and Fast-Moving — Thrives in ambiguity, learns new frameworks (LangChain, LlamaIndex, etc.) quickly, and delivers results at startup speed. Works seamlessly with founders, engineers, and product teams to iterate rapidly.
- Examples of Candidates (do not contact)
Must-Have Requirements
5+ years of experience in Machine Learning (PyTorch, TensorFlow). 2+ years of hands-on experience working with LLMs (Hugging Face, OpenAI, Anthropic). Proven track record of building and deploying production AI systems, including RAG and vector search. Strong understanding of AI safety, prompt engineering, and content filtering best practices. Excellent communication skills, with experience collaborating directly with users, clients, or cross-functional teams. Familiarity with Rails is a plus, but not required — strong candidates can ramp up quickly.
Screening Questions
Common Rejection Reasons
Experience Level: The candidate demonstrates mid-level experience signals but the role requires senior or staff-level impact and ownership across full product lifecycle. Startup Experience: The candidate has primarily worked at larger organizations and lacks recent startup experience (preferably at companies with $7M–$8M ARR or similar stage). Enterprise Exposure: The candidate has not worked on products selling to enterprise customers, which is a core requirement for Arist's product environment. Culture Fit: The candidate may not align with Arist’s culture emphasizing caring, kindness, ambition, adaptability, and high learning agility. Communication & Collaboration: The candidate did not demonstrate strong product sense or ability to communicate effectively with customers, clients, or cross-functional teams. Compensation: The candidate’s expected salary exceeds the range for this role given their experience and impact level. Location: The candidate does not reside within the U.S. or in a commutable time zone and is not open to relocation. Interest Alignment: The candidate expressed limited interest in user-facing or customer-interacting aspects of the role, which are essential for success at Arist.
These are common reasons why candidates have been rejected for this position. Consider these when selecting candidates to submit.
About the Company
Company Overview
Our Mission
Company Culture
Benefits
Relocation & Sponsorship
What you can expect
Day to Day
You’ll spend your days working directly with customers to design, build, and deploy advanced AI systems that deliver immediate real-world impact. Your time will be split between hands-on development (Rails, ML/LLM workflows), collaborating with client teams to understand their workflows, and rapidly iterating on solutions based on real feedback. Most days blend deep technical problem solving with fast feedback loops from both internal teams and enterprise customers, giving you direct visibility into how your work drives product adoption and impact.
Team
You’ll join a lean, highly technical founding team, reporting directly to the CTO and collaborating closely with the CEO and product leads. The team is composed of engineers with backgrounds in AI/ML, backend systems, and enterprise software, all focused on building production-grade AI enablement tools. You’ll have significant ownership and autonomy, with the ability to shape architecture, standards, and culture.
Growth
This is a high-impact role with significant opportunities for advancement. You’ll have the chance to:
- Take ownership of core technical deployments from day one.
- Work closely with enterprise customers to influence product direction in real time.
- Mentor and support other engineers as the team scales.
- Build a strong track record of shipping and deploying AI systems at scale — positioning yourself for future leadership roles in engineering, product, or customer success functions.
Interview Process
Initial Call (30 min) – Introductory conversation with a recruiter or founder to review background, motivations, and role fit.
Technical Deep Dive (60–90 min) – Live discussion with senior engineers covering LLM design scenarios, Rails integration, and system-level problem solving.
Practical Exercise / Case Study – Short take-home or live coding assignment focused on building or evaluating an AI workflow.
Final Round (60 min) – Cultural and cross-functional interviews with founders/team to assess collaboration, communication, and alignment with mission.
Companies to Source From
These companies are similar to our client. Candidates with experience at these companies are seen as a big plus.
Additional Information
Additional Information
This is a high-visibility, customer-facing role where you’ll work directly with enterprise clients to deploy and shape cutting-edge AI products in real environments. The team moves quickly and values clear communication, technical excellence, and ownership.
The interview process typically includes a technical deep dive, a practical exercise, and conversations with founders and engineers to assess both technical skill and ability to collaborate with customers. Ideal candidates are comfortable working in fast-paced environments and enjoy solving complex problems hands-on with users.
We’re actively interviewing and will move quickly for strong candidates.
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