Gentoro | Senior ML Engineer

About The Role

We are looking for a visionary Senior ML Engineer who will bridge the gap between high-level architecture and hands-on execution, specifically focusing on simplifying enterprise integration for AI agents. As a key hire during our current growth phase, you will define the standards for how our platform scales and interacts with other enterprise applications.

What You'll Do

• Design and implement multi-agent systems and orchestration layers
• Build and operate observability stacks (e.g., OpenTelemetry) to monitor agent reasoning paths, tool usage, and performance in real-time
• Develop and enforce technical safety mechanisms—such as input/output filtering and behavioral boundaries—to mitigate risks like hallucinations, prompt injections, and bias
• Implement fallback mechanisms, human-in-the-loop (HITL) checkpoints, and automated recovery for agentic failures
• Implement best practices for LLMOps, monitoring, and performance tuning of AI models in live environments
• Automate SDLC processes and CI/CD pipelines, elevate QA standards, and develop incident response protocols to enable high velocity, availability and reliability of our platform

Who You Are

• You thrive in the "ambiguity phase" of a startup but build with the discipline of an enterprise-grade engineer
• You are a "force multiplier" who elevates the technical bar for the entire team
• You are obsessed with the practical application of AI, moving beyond demos to create reliable, production-hardened products
• You have a high-confidence, low-ego mindset

Requirements

• 5+ years of senior engineering experience at a fast-paced, high-growth technology startup that has successfully scaled from early stage through Series A/B funding (or equivalent growth phase)
• 2+ years of ML, specifically training or fine-tuning LLM models, embeddings; building clustering models; utilizing evaluation frameworks to quantify performance
• Proficiency in agent orchestration and memory-augmented systems
• Experience using feedback loops to continuously improve ML systems
• Thrives in startup ambiguity while maintaining the discipline of an enterprise-grade engineer
• Acts as a force multiplier who elevates the technical bar for the entire team
• Obsessed with practical application of AI systems and capable of building enterprise solutions that solve real-world customer problems