About Invyte, Inc

Job Description

Key Responsibilities

• Build agents end to end: LLM orchestration, tool use across a growing set of MCP integrations, retrieval, and multi-agent workflows that automate real investigations.
• Own the layer that makes agents trustworthy: eval harnesses, guardrails, audit trails, and human-in-the-loop review.
• Ship fast and close to users: code in production your first week, direct customer contact, and work that catches actual fraud.
• Experiment relentlessly with new models, techniques, and tooling, and keep what wins.
• Turn frontier capabilities into product by probing what the newest models can do and shipping the parts that survive contact with real fraud cases.
• Shape roadmap and strategy with the founders in a small, zero-bureaucracy team with maximum ownership.

Qualifications

• A systems thinker who reasons about data flow, failure modes, latency, cost, and what breaks at 10x.
• Has shipped real things with LLMs, such as production systems, prototypes with users, or serious side projects.
• Deep engineering fundamentals: can design, ship, debug, and operate a production system end to end.
• 0 to 1 builder who is energized by ambiguity and speed.
• Roughly 2–4 years of engineering experience, or a portfolio that demonstrates equivalent ability.
• Prior startup experience is a plus.
• Curiosity about fintech, fraud, or building AI in regulated, high-stakes environments.

About Invyte, Inc

Job Description

Key Responsibilities

• Build agents end to end: LLM orchestration, tool use across a growing set of MCP integrations, retrieval, and multi-agent workflows that automate real investigations.
• Own the layer that makes agents trustworthy: eval harnesses, guardrails, audit trails, and human-in-the-loop review.
• Ship fast and close to users: code in production your first week, direct customer contact, and work that catches actual fraud.
• Experiment relentlessly with new models, techniques, and tooling, and keep what wins.
• Turn frontier capabilities into product by probing what the newest models can do and shipping the parts that survive contact with real fraud cases.
• Shape roadmap and strategy with the founders in a small, zero-bureaucracy team with maximum ownership.

Qualifications

• A systems thinker who reasons about data flow, failure modes, latency, cost, and what breaks at 10x.
• Has shipped real things with LLMs, such as production systems, prototypes with users, or serious side projects.
• Deep engineering fundamentals: can design, ship, debug, and operate a production system end to end.
• 0 to 1 builder who is energized by ambiguity and speed.
• Roughly 2–4 years of engineering experience, or a portfolio that demonstrates equivalent ability.
• Prior startup experience is a plus.
• Curiosity about fintech, fraud, or building AI in regulated, high-stakes environments.