Associate AI Engineer

About the Role

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.

Join a high-growth team where curiosity is valued, ownership is encouraged, and your work drives real-world impact. This role is fully in-office in Los Angeles. 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.

Requirements & Tech Stack

• 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.

Qualifications

• Bachelor’s or Master’s in Computer Science or a related field, or equivalent practical experience (bootcamp, self-taught, or on-the-job).
• Roughly 0–2 years of software engineering experience; internships, research, and substantial projects count.
• A track record of learning quickly, shown through coursework, projects, work, open-source contributions, or community involvement.