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Retrieval Augmented Generation Rag Jobs in New York

Tavily Inc

Forward Deployed Engineer

New York, NY · On-site

Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help ...

Pearson

Staff Software Engineer

Hoboken, NJ · Hybrid

$150K - $180K/yr

Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering techniques to improve learning personalization and response quality. * Evaluate emerging AI ...

Pearson Education

Staff Software Engineer

Hoboken, NJ · On-site

$150K - $180K/yr

Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering techniques to improve learning personalization and response quality. * Evaluate emerging AI ...

Photon

Gen AI Engineer - NY

Manhattan, NY

Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., Pinecone, FAISS) * API development and integration (FastAPI, REST, microservices) * Cloud platforms: AWS / Azure / GCP ...

Cross River

Senior AI/ML Engineer

Fort Lee, NJ · On-site

$160K - $200K/yr

Work with LLMs, embedding models, and Retrieval-Augmented Generation (RAG) system * Engineer and refine prompts to enhance AI performance and output quality * Deploy and scale AI solutions using AWS ...

USG, Inc.

Distinguished AI Engineer

Jersey City, NJ · On-site

Define the target-state architecture for enterprise AI platforms, LLM platforms, model hubs, AI gateways, Retrieval-Augmented Generation (RAG) services, agentic AI frameworks, orchestration layers ...

Pearson Education

Staff Software Engineer - AI

Hoboken, NJ · On-site

$160K - $175K/yr

Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering techniques to improve learning personalization and response quality. * Evaluate emerging AI ...

Pearson

Staff Software Engineer

Hoboken, NJ · Hybrid

$160K - $175K/yr

Design and implement Retrieval-Augmented Generation (RAG) pipelines. * Apply advanced prompt engineering techniques to improve learning personalization and response quality. * Evaluate emerging AI ...

Photon

Gen AI Engineer - NY

Manhattan, NY · On-site

Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., Pinecone, FAISS) * API development and integration (FastAPI, REST, microservices) * Cloud platforms: AWS / Azure / GCP ...

Omni Inclusive

Agentic AI & Python Developer

Secaucus, NJ · On-site

$51 - $70.25/hr

Develop and maintain prompt engineering, retrieval-augmented generation (RAG), and memory management for AI agents. * Optimize performance, reliability, and cost-effectiveness of AI-driven solutions.

LifeMD

VP, Data & Analytics

Manhattan, NY · On-site

$198K - $255K/yr

Build secure enterprise Retrieval-Augmented Generation (RAG) platforms leveraging proprietary healthcare knowledge and enterprise content * Design, deploy, and manage AI agents that automate clinical ...

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Forward Deployed Engineer

Tavily Inc

New York, NY • On-site

Full-time

Posted 8 days ago

Job description

About Tavily
We're building the search engine for AI agents. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent - but also informed.
We work with some of the most innovative teams in AI - from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.
Role Overview
This is a full-time, on-site role for a Forward Deployed Engineer based in our New York office.
We're looking for a technically exceptional engineer who's excited to work directly with our customers and partners to design, prototype, and deploy integrations of Tavily's API into real-world agentic systems. As a Forward Deployed Engineer, you'll be embedded with strategic partners and high-value customers-playing a critical role in both pre-sales and post-deployment success. You'll help build RAG pipelines, production-grade applications, and industry-specific GenAI use cases built on Tavily.
You'll operate across the customer lifecycle-from early discovery and solution design to scaling usage and identifying new opportunities. You'll bridge product, engineering, sales, partnerships, and customer success.
Responsibilities
  • Work directly with customers to understand their use cases and build custom integrations using Tavily's API.
  • Participate in pre-sales efforts, including sales calls, technical discovery, and proof-of-concept scoping.
  • Design and implement prototypes, proofs of concept, and production-ready systems alongside client teams.
  • Monitor API usage patterns and recommend improvements to increase reliability, efficiency, and coverage.
  • Collaborate with internal teams to influence roadmap priorities based on customer needs and recurring technical challenges.
  • Build reusable technical content including documentation, reference architectures, internal tools, and templates to scale future deployments.
  • Represent Tavily in customer conversations, architecture reviews, post-deployment tracking, and partner-facing technical engagements.

Ideal Profile
  • 3+ years of software engineering experience, ideally in a customer-facing, consulting, or partner-focused role at a large company (e.g., Forward Deployed Engineer, Solutions Architect, Sales Engineer).
  • Obsession with AI ecosystem - strong engineering skills and experience working with the modern agent stack.
  • Deep understanding of agent architectures, Retrieval-Augmented Generation (RAG), LLMs, context engineering, and .
  • Strong interpersonal skills and comfort working directly with technical stakeholders at enterprises and partners.
  • Fast learner, highly autonomous, and excited to work in a fast-paced startup environment.
  • Based in New York City, Austin, SF, or willing to relocate.

Interview Process
  1. Intro Call - An informal chat to get to know you, your background, and your interest in AI.
  2. Take-Home Exercise - A short assignment similar to the work you'd do on the job.
  3. Live Demo - A live demo to present the use case you built, your approach, and the business and technical value.
  4. Final Meeting - A conversation with our leadership team.

Apply