Forward Deployed Engineers

Conquer AI; New York (NY); Hybrid; USD 92,000 - 221,000; Part time

Job summary

Conquer AI in New York City is seeking Forward Deployed Engineers to work directly with clients, designing and delivering full-stack AI solutions powered by LLMs and agentic workflows.

You will own projects from discovery to deployment, collaborating closely with client teams and adapting quickly as requirements evolve in a fast-moving AI environment.

Qualifications

• 8+ years in software engineering overall.
• 5+ years in full-stack development (React, SQL, Python or TypeScript).
• 2+ years shipping LLM or generative AI apps in production.
• 1+ year with agent orchestration (LangChain, LangGraph, or CrewAI).
• 1+ year designing RAG pipelines and using vector stores.
• Strong client-facing communication and decison-making.

Responsibilities

• Scope client problems and translate to technical solutions.
• Act as technical client contact and explain progress and trade-offs.
• Build and deploy production-ready full-stack apps.
• Develop LLM features, AI agents, and workflows in client environments.
• Build backend services and frontend interfaces that endure real use.
• Run prompt engineering and evaluation to improve delivered work.

Skills

Software engineering

AI engineering

Full-stack development

React

TypeScript

Python

SQL

Node.js

LLM apps

Agent orchestration

RAG pipelines

Vector databases

Cloud platforms

Tools

LangChain

LangGraph

CrewAI

Pinecone

Qdrant

pgvector

LangSmith

Braintrust

Terraform

Job description

Hybrid, New York (Manhattan) | Contract | Competitive day rate

You must hold full working rights in the United States. We are unable to sponsor visas, and we do not consider visa holders working through a C2C arrangement.

We will also require you to complete a Technical Assessment.

About the role

We are looking for a number Forward Deployed Engineers to work alongside our AI Solutions Lead, building production AI applications directly for our clients.

You will partner with the client to understand their operational problems, then design and ship full-stack solutions powered by LLMs and agentic workflows. This is a client-facing role, not a back-office build role. You will own projects from discovery through deployment, working closely with the client team and iterating fast as requirements shift.

What you will do

• Work directly with our client to scope problems and turn them into technical solutions
• Act as the day-to-day technical contact for the client team, explaining progress and trade-offs with confidence
• Build and deploy production-ready applications with modern full-stack tooling
• Develop LLM features, AI agents, and intelligent workflows inside customer environments
• Build backend services and frontend experiences that hold up under real use
• Run prompt engineering and evaluation cycles to keep improving what's shipped

What you will bring

• Solid background in software engineering or AI engineering, ideally with roots in solutions engineering, implementation, or embedded delivery work before AI made it fashionable
• 8+ years in software engineering overall
• 5+ years in full-stack development (React, SQL, and Python or TypeScript as your main language, with working fluency in the other)
• 2+ years hands-on shipping LLM or generative AI applications in production
• 1+ year working with agent orchestration frameworks (LangChain, LangGraph, or CrewAI)
• 1+ year designing RAG pipelines and working with vector databases
• Comfortable translating business needs into technical decisions with minimal hand-holding
• Genuine interest in where this field is heading, and the ability to adapt fast when the tools change
• Strong communication skills and real confidence dealing with clients. You'll be the engineer customers see and trust

You keep pace with how fast AI is moving, and you have actually used the latest tools, not just read about them.

Core languages and backend

• Python: for AI orchestration, data scripting, and backend logic
• TypeScript / Node.js: for customer-facing extensions, full-stack glue code, and lightweight web UIs
• React / Next.js: for customer-facing interfaces
• SQL: for deep database queries (Java or Go is a plus for heavy enterprise system extensions)
• FastAPI / Flask: for spinning up microservices and rapid integration layers

AI and agent orchestration

• LLM providers: fluency with Anthropic Claude, OpenAI, and open-source models via Hugging Face or vLLM
• Orchestration: production proficiency in LangChain, LangGraph, or CrewAI for multi-step agent behaviours
• RAG and vector stores: hands-on design with vector databases (Pinecone, Qdrant, pgvector) and retrieval systems
• Evaluation and observability: deployment tracking using tools like LangSmith or Braintrust to audit hallucination rates and latency

Data and infrastructure

• Databases and warehouses: PostgreSQL, Snowflake, or BigQuery for customer data ingestion
• Cloud platforms: working knowledge of AWS, GCP, or Azure
• Containerisation: Docker as a baseline, with basic Kubernetes and Terraform for replicating environments