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Fuel Cycle

Associate AI Engineer (Onsite in Los Angeles)

Los Angeles OfficeUnited States
Junior · FullTime · Engineering
POSTED 27 OCT
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Pay · posted
$90k–110k
mkt median $203k
Open for
10d
since 27 Oct
H-1B · FY25
1
certified LCAs
Hiring · 30d
15
0 net · open roles
PythonDjangoFastAPIPostgreSQLRedisAWSSQLLLMs

About Fuel Cycle:

Fuel Cycle empowers leading organizations with agile research solutions that deliver decision-ready insights — fast, flexible, and fully integrated. As a market research disruptor, our AI-powered Insights Platform is built for speed, precision, and scale. With cutting-edge tools and seamless audience connectivity, we help brands ditch the guesswork and make smarter, customer-led decisions at lightning speed.

Why work at Fuel Cycle?

Join a high-growth team where curiosity is valued, ownership is encouraged, and your work drives real-world impact. Whether you’re based at our Los Angeles HQ, New York City hub, working remotely across the U.S., or part of our global team in India, you’ll help shape the future of decision intelligence for some of the world’s most iconic brands.

Overview

Build real AI agents on a production platform, with senior engineers working alongside you. You will work in Python and FastAPI, build tool-calling agents on AWS Bedrock and AgentCore using the Strands Agents framework, and own technical decisions within the problems you take on, with your scope growing as you do. This role is fully in-office in Los Angeles because our mentorship model depends on it: pair programming, in-person code review, and a mentor a few desks away.

We hire for fundamentals, curiosity, and initiative. This is an early-career role. Engineers new to the field are welcome, and so are engineers with a year or two of experience who want to go deeper into AI and agentic systems. You do not need to know our stack on day one.

What You’ll Work On

Design, build, and improve AI agents, deciding how tool-calling loops, model configuration, and multi-step workflows are structured using Strands Agents and AWS Bedrock

Build and integrate MCP tools and servers, and learn how tool names and descriptions shape what an LLM does

Deploy and run agents on Bedrock AgentCore, working with the platform team and your mentor

Build RAG pipelines over pgvector or FAISS, choosing chunking, embedding, and retrieval approaches based on what the results show

Design, test, and iterate on prompts; evaluate answer quality, latency, and token cost, and decide which tradeoffs to make; learn how guardrails are applied in production paths

Evaluate models, tools, and approaches, and recommend which to use, with your reasoning documented

Ship features and bug fixes in FastAPI services (REST and WebSocket APIs backed by PostgreSQL and Redis), deciding how to design, implement, and test them

Contribute to background jobs and queue consumers so agent work runs outside the API request path

Apply our architectural standards, use your judgment on design choices within them, help keep design documents current, and flag where the standards should change

Submit pull requests, respond to review feedback, and review teammates’ code as you grow

What You Bring

Solid Python from coursework, internships, or work: functions, classes, type hints, error handling, testing basics

Data structures and algorithms, with awareness of time and space complexity

SQL fundamentals (joins, aggregations, indexes, schema design); PostgreSQL exposure preferred

REST and HTTP concepts, and how a web API is structured

Git and pull-request workflow

Clear communication, comfort asking questions, and a habit of picking up new tools and asking for help when you’re stuck

Preferred

An agent project built with Strands Agents, LangChain, the OpenAI SDK, or a similar framework (coursework, personal, or professional), including how an LLM chooses and calls tools

Model Context Protocol (MCP): building or using MCP tools and servers

AWS Bedrock or Bedrock AgentCore, or AWS basics such as S3 and SQS

RAG: chunking, embeddings, and vector stores

Prompt engineering: writing, testing, and iterating on prompts

FastAPI or a similar framework such as Flask or Django, SQLAlchemy or another ORM, and a willingness to learn async Python

Experience

Bachelor’s or Master’s in Computer Science or a related field, or equivalent practical experience (bootcamp, self-taught, or on-the-job)

Source · Fuel CycleFirst seen 2 NovOriginal ↗