Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Best Practices, Bridge Building, Civil Engineering, Continuous Deployment/Delivery, Continuous Improvement, Continuous Integration, Engineering, Enterprise Applications, Funding, High Availability, High Level Architecture (HLA), Incident Response, Injections, Input/Output, Leadership, Memory Hardware, Oracle WebLogic Server, Performance Analysis, Performance Tuning/Optimization, Product Demonstration, Quality Assurance, Risk Management, Software Development Lifecycle (SDLC), Splunk, Standards Development, Startup
About Us
Gentoro was founded by a team with deep experience in enterprise infrastructure and AI, with leadership roots at companies including Splunk, WebLogic, and Asurion. Gentoro helps organizations simplify AI integration into real-world systems, with the observability, manageability, and security required for production deployments. As agentic workflows shift from experimentation to real execution, Gentoro helps teams enforce governance, maintain auditability, and deliver reliable outcomes at scale.
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
- Build visualizations to convey interesting behavior of agentic systems
- 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