Senior Forward Deployed Engineers

Conquer AI; United States; On-site; USD 140,000 - 220,000; Full time

Job summary

Conquer AI is seeking a Senior Forward Deployed Engineer to work directly with clients, designing and delivering production AI applications powered by LLMs and agentic workflows. You will own projects from discovery to deployment, collaborating with client teams and iterating quickly as requirements evolve.

Ideal candidates have 8+ years in software engineering, 5+ years in full-stack development (Python/TypeScript, React, SQL), and hands-on experience shipping LLM/generative AI solutions.

Qualifications

• 8+ years in software engineering overall.
• 5+ years in full-stack development (Python and/or TypeScript, React, SQL).
• 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.

Responsibilities

• Work directly with our client to scope problems and turn them into technical solutions.
• 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.

Skills

Software engineering

AI engineering

Full-stack development

Python

TypeScript

React

SQL

LLM apps

Agent orchestration

LangChain

Tools

LangChain

LangGraph

CrewAI

Pinecone

Qdrant

pgvector

LangSmith

Braintrust

Job description

You must hold full working rights in the United States. We are unable to sponsor visas for this role.

We will also require you to complete a Technical Assessment.

About the role

We are looking for a number of Senior 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 isn't 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
• 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
• Someone who tracks the AI space closely and adapts fast when the tools change
• 5+ years in full-stack development (Python and/or TypeScript, React, SQL)
• 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, not just the current toolset
• Strong communication skills. 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: non-negotiable for AI orchestration, data scripting, and backend logic
• TypeScript / Node.js: essential for building customer-facing extensions, full-stack glue code, and lightweight web UIs
• SQL and Java/Go: needed for deep database queries and 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 or LangGraph and 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.