10+ years of senior engineering experience in high-growth tech startups
5+ years in machine learning, with 2+ years on large language models or agent workflows
Proven skills in agent orchestration and memory-augmented systems
Hands-on expertise in analyzing tracing and logging data
Experience improving ML systems through feedback loops
History of building agents that interface with enterprise data sources
Proficiency in modern tech stack: Python, semantic search, vector databases, GraphQL, Kubernetes, and real-time data processing
Responsibilities
Design and implement multi-agent systems and orchestration layers
Build and operate observability stacks for real-time monitoring of agent performance
Develop safety mechanisms to mitigate risks in AI interactions
Analyze telemetry and execution traces for continuous agent improvement
Connect agents securely to external services and enterprise APIs
Implement human-in-the-loop checkpoints and automated recovery protocols
Elevate quality assurance standards and develop incident response protocols
Benefits
Join a team with deep expertise in AI and enterprise infrastructure
Opportunity to define standards for enterprise application interactions
Contribute to simplifying AI integration in real-world settings
Work in a growth phase with potential for significant impact
Be part of a visionary team focused on practical AI applications
Full Job Description
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 Principal 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.
Analyze telemetry and execution traces to create feedback loops for continuous agent improvement and automated evaluation.
Securely connect agents to external services, unstructured data, and enterprise APIs via robust tool-calling schemas.
Implement fallback mechanisms, human-in-the-loop (HITL) checkpoints, and automated recovery for agentic failures.
Implement best practices for MLOps, 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
Requirements
10+ 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)
5+ years of ML, including 2+ years focused on LLMs or agentic workflows.
Proficiency in agent orchestration and memory-augmented systems.
Hands-on experience analyzing tracing and logging data.
Experience using feedback loops to continuously improve ML systems
Built agents that invoked tools or utilized Model Context Protocol (MCP) to access enterprise data sources
Proficiency in modern technologies (e.g., Python, semantic search, vector DBs, GraphQL, queues, containers, Kubernetes, real-time data processing, Spark, Open Telemetry, Clickhouse)
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
