Level

Tavily

Forward Deployed Engineer, Enterprise

Tavily is hiring a Forward Deployed Engineer, Enterprise in New York, United States. It pays $180k-$224k a year and Level rates it ; you can apply on Tavily.

Level's read on this job

  • Level rates this forward deployed engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • Pay on this posting starts in line with the typical forward deployed engineer posting on Level.
  • The posting mentions OpenAI, LangChain and LlamaIndex among its tools and skills.
  • Level classes it as a senior role.
  • Tavily has 12 other open roles on Level.

AI in this role

Design and deploy production-grade AI agent systems and RAG pipelines for enterprise customers as a Forward Deployed Engineer.

openailangchainllamaindexlanggraphcrewaipineconeweaviateqdrantpgvectortavily-apillms
ragai-agentsrag-pipelinesagent-workflowssolution-designenterprise-sales-engineering

About Tavily 

We're building the search engine for AI agents. Our API powers agentic applications and real-time reasoning by connecting LLMs to high-quality, trustworthy web content. We work with some of the most innovative teams in AI — from startups shaping the ecosystem to enterprises deploying AI at scale. 

The Team

Forward Deployed Engineering owns how Tavily shows up technically in the field. The team sits at the intersection of customers, Product, Engineering, Sales, Partnerships, and Customer Success. 

The Enterprise FDE motion focuses on Tavily’s highest-value customers and strategic enterprise opportunities — from technical discovery and solution design through proof of concept, production rollout, adoption, and expansion. 

The role

This is a full-time, on-site role based in our New York office. 

As a Forward Deployed Engineer, Enterprise, you’ll work directly with Tavily’s largest and most strategic customers to design, prototype, and deploy production-grade AI systems powered by Tavily’s API. You’ll own the technical relationship across the customer lifecycle, helping enterprise teams move from early use case exploration to deployed, scalable agentic applications. 

You’ll be deeply hands-on: building RAG pipelines, agent workflows, evaluation loops, reference architectures, and custom integrations alongside customer teams. You’ll also serve as a critical bridge between the field and our Product and Engineering teams, translating enterprise needs into roadmap signal. 

Your responsibilities will include: 

  • Work directly with enterprise customers to understand their AI use cases, technical architecture, success criteria, and deployment requirements. 
  • Lead technical discovery and solution design during pre-sales and expansion conversations. 
  • Scope, build, and deliver proofs of concept that demonstrate clear business and technical value. 
  • Design and implement production-ready integrations using Tavily’s API, including RAG pipelines, agent workflows, internal tools, and industry-specific GenAI applications. 
  • Partner with customer engineering, data, product, and AI teams to move use cases from prototype to production. 
  • Monitor customer API usage patterns and recommend improvements to increase reliability, latency, coverage, and overall value. 
  • Translate recurring customer needs, blockers, and technical patterns into clear product and roadmap input. 
  • Create reusable enterprise assets, including reference architectures, integration templates, deployment guides, demo environments, and technical documentation. 
  • Represent Tavily in customer architecture reviews, executive technical conversations, implementation check-ins, and post-deployment reviews. 
  • Partner closely with Sales, Customer Success, Product, and Engineering to drive adoption, retention, and expansion across strategic accounts. 

We expect you to have: 

  • 3+ years of software engineering experience, ideally in a customer-facing technical role such as Forward Deployed Engineer, Solutions Architect, Solutions Engineer, Sales Engineer, or Technical Consultant. 
  • Strong hands-on engineering ability, especially with Python, APIs, backend systems, and production software development. 
  • Experience building with LLMs, Retrieval-Augmented Generation, agent architectures, context engineering, and modern AI application stacks. 
  • Experience working with enterprise customers, including technical discovery, POCs, solution design, stakeholder management, and production rollout. 
  • Strong understanding of how enterprises evaluate, deploy, secure, and scale AI systems. 
  • Ability to communicate clearly with both technical and executive stakeholders, including engineering leaders, product teams, AI teams, and technical decision-makers. 
  • High autonomy, strong ownership, and comfort operating in a fast-moving startup environment. 
  • Excellent written and verbal communication skills, with the ability to turn complex technical concepts into clear recommendations. 
  • Based in New York City, or willing to relocate. 

It will be an added bonus if you have: 

  • Experience working with major agent or LLM orchestration frameworks such as LangChain, LlamaIndex, LangGraph, OpenAI Agents SDK, or CrewAI. 
  • Experience with vector databases such as Pinecone, Weaviate, pgvector, Qdrant, or similar systems. 
  • Experience with enterprise AI use cases in financial services, legal, consulting, enterprise SaaS, sales, marketing, or internal knowledge management. 
  • Track record helping large customers move from POC to production. 
  • Experience building internal tools, technical playbooks, demos, or reusable customer-facing assets. 
  • Prior experience at a high-growth AI infrastructure, developer tools, or enterprise SaaS company. 

Compensation

We offer competitive compensation packages. The actual compensation offered within the listed range will be determined based on job-related factors, including experience, skills, qualifications, and the level at which the candidate is hired, consistent with applicable law.

Pay Transparency

We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.

Base Compensation Range$179,500—$224,300 USD

Pay Transparency

We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.

On Target Earnings Range$179,500—$224,300 USD

How we rate this

Forward Deployed Engineer, Enterprise at Tavily rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsRAG PipelinesAgent WorkflowsSolution DesignEnterprise Sales EngineeringOpenAILangChain

Also common in forward deployed engineer jobs at this level

Pay

This job starts at
$180k
Typical forward deployed engineer job
$182k

This posting starts close to the typical forward deployed engineer job. See forward deployed engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. Tell me about a project where rag pipelines was part of your work. What did you do?
  4. Tell me about a project where agent workflows was part of your work. What did you do?
  5. Tell me about a project where solution design was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, RAG Pipelines, Agent Workflows, and Solution Design. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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