AI Engineer

Wasserman•Los Angeles, CA

1d•$130 - $160

About The Position

The-Team is a culture-centric agency, serving the best athletes, artists, brands & properties in the world. We are seeking an AI Engineer – LLM Systems to help build the next generation of AI-powered systems and agents that transform how teams work across sports, media, talent, and brand consulting. This is a hands-on role focused on building real-world AI systems, not research. You’ll design and deploy agent-based workflows, retrieval systems, and intelligent services that power internal tools and decision-making across the organization. If you’ve built AI systems that people actually use and want to shape how AI is deployed across an enterprise—we’d love to connect.

Requirements

3–6 years of experience in software engineering or applied AI, with a strong foundation in building production systems
Strong proficiency in Python and experience building backend services and APIs
Hands-on experience developing and deploying LLM-powered applications in production environments
Experience implementing RAG pipelines, including document ingestion, chunking strategies, embedding workflows, and retrieval optimization
Experience designing prompt architectures and structured outputs (e.g., JSON schemas, tool/function calling)
Familiarity with building multi-step workflows or agent-like systems (not just single prompts or chat interfaces)
Ability to take ambiguous business problems and translate them into structured AI workflows and systems
Experience working with real-world data constraints (messy data, incomplete context, edge cases)
Strong understanding of tradeoffs between accuracy, latency, and cost in LLM systems
Demonstrated ability to ship quickly, iterate, and improve systems based on usage

Nice To Haves

Experience building retrieval systems over large datasets (e.g., embeddings, semantic search)
Experience designing multi-step AI workflows or integrating LLMs with tools, APIs, and data sources
Experience working with unstructured enterprise data (documents, reports, transcripts)
Experience integrating AI systems with APIs and enterprise tools
Experience evaluating and improving LLM system reliability over time

Responsibilities

Design and build AI systems that can reason, retrieve information, and take action across enterprise tools and data sources
Translate business use cases into structured workflows, defining task decomposition, tool usage, and input/output schemas
Evaluate and prioritize AI use cases based on feasibility, data readiness, and business impact
Define MVP scope and sequence development of AI systems based on prioritized use cases
Develop and optimize LLM-powered systems, including prompt architectures, structured outputs, and multi-step reasoning workflows
Build and scale retrieval pipelines (RAG) across structured and unstructured data (chunking, embeddings, ranking, hybrid search)
Define how AI systems and workflows are orchestrated and structured, enabling reusable and composable capabilities as the platform evolves
Build reusable AI services and APIs that power internal applications and automation
Turn early prototypes into reliable, production-grade systems used by internal teams
Evaluate and improve system performance across accuracy, latency, cost, and reliability in production environments