Operations Associate

Arrows; Melbourne; On-site; AUD 120,000 - 180,000; Full time

Job summary

Arrows is seeking a skilled AI engineer to design and ship live agent systems for gaming experiences. You will work with a live product, selecting approaches that balance cost, control and speed, then validate outcomes with real users and stakeholders.

You’ll partner with leadership to drive AI strategy, run office hours, demos and internal training, ensuring scalable, observable AI systems across the platform.

Qualifications

• Demonstrable impact from agentic or LLM powered systems you've shipped to real users.
• Hands on experience with an agent framework such as LangGraph, LlamaIndex, Semantic Kernel or ADK, and RAG in production, including embedding models, vector stores, re ranking.
• Strong Python experience on a real engineering foundation: testing, version control, CI/CD, and the APIs that serve your own work.
• Hands on experience with a major cloud and its managed AI services, Azure and AI Foundry or the GCP/AWS equivalents, plus solid SQL and relational modelling.
• Architectural judgement, making the design call, defending the trade offs, and knowing where an LLM system needs optimising on cost, latency and output.
• Strong product sense, data driven thinking, and an understanding of what players actually need.

Responsibilities

• ow people actually want to engage with sport, together. The comparison the founders draw is Revolut disrupting Barclays or Robinhood disrupting Etrade; this team is executing the same playbook against the traditional gaming sector.

Skills

Agent frameworks

LLM/RAG in production

Python

Cloud AI services

SQL

Architecture judgement

Product sense

Tools

LangGraph

LlamaIndex

Semantic Kernel

ADK

Job description

ow people actually want to engage with sport, together. The comparison the founders draw is Revolut disrupting Barclays or Robinhood disrupting Etrade; this team is executing the same playbook against the traditional gaming sector.

What you'd be building

Agents that run in a live product, not a slide deck, taken from experiment to something the team is happy to put in front of players, tested and validated to the standard a live product demands. The shared frameworks, templates and infrastructure that let other engineers ship their own agents without starting from scratch. The technical shape of the entire agent estate: how it retrieves, how it evaluates, how it constrains behaviour, how it's monitored, and how any of it reaches production. The sign off process for what goes live, how it handles player data, and where its authority stops.

How you'd work

Sitting with the teams who feel the pain, finding where an agent would genuinely pay for itself, and getting something in front of them fast enough to learn whether you were right. Choosing the approach, low code, pro code, or off the shelf, weighed on cost, control and speed to land. Designing for the failure cases: a call that times out, a tool that errors, a decision the agent should hand back to a person. Acting as the company's AI champion, running office hours, demos and short training sessions that make the wider team better at using AI. With direct access to senior leadership and real influence over where AI goes next, not a backlog someone else wrote.

What they need

Demonstrable impact from agentic or LLM powered systems you've shipped to real users, with the ability to explain what broke and what you changed. Hands on experience with an agent framework such as LangGraph, LlamaIndex, Semantic Kernel or ADK, and RAG in production, including embedding models, vector stores, re ranking, and knowing when a live query beats retrieval. Strong Python experience on a real engineering foundation: testing, version control, CI/CD, and the APIs that serve your own work. Hands on experience with a major cloud and its managed AI services, Azure and AI Foundry or the GCP/AWS equivalents, plus solid SQL and relational modelling. Architectural judgement, making the design call, defending the trade offs, and knowing where an LLM system needs optimising on cost, latency and output that only sounds right. Strong product sense, data driven thinking, and an understanding of what players actually need.

Nice to have

Proving an AI system behaves rather than trusting it: eval sets, output scoring, tracing, regression gates. Retrieval pipelines at volume: embedding at scale, index freshness, accuracy as underlying data moves. Experience with workflows that survive contact with reality: timeouts, failed APIs, a human approving a step.

Responsibilities

ow people actually want to engage with sport, together. The comparison the founders draw is Revolut disrupting Barclays or Robinhood disrupting Etrade; this team is executing the same playbook against the traditional gaming sector.