AI Engineer

About the Role

Teserac is building neuron™, a unified AI-native platform for data center observability, intelligence, and workflow automation. neuron™ processes real-time telemetry from thousands of sensors, meters, and control systems across heterogeneous environments — giving infrastructure owners the visibility to monitor, analyze, automate, and proactively manage power operations with full situational awareness. An embedded AI teammate serves as every operator's always-on co-pilot: detecting anomalies, correlating events, and surfacing recommendations 24/7.

We are seeking an AI/ML Engineer who is excited to build intelligent systems at the intersection of applied AI and critical infrastructure. You will work across the full AI development lifecycle — from data pipelines and model integration to agentic orchestration, evaluation, and production support — collaborating closely with a small, fast-moving engineering team.

This is not a research-only role, but research thinking matters here. You will be expected to read papers, stay ahead of the field, and bring ideas to the table — then build them into production systems.

Who We Are Looking For

We care more about how you think than how many years are on your resume. This role is open to both junior and senior candidates. What matters is:

You are genuinely excited about AI and infrastructure — not just one of them

You learn fast, go deep, and can hold your own in a technical debate

You have the engineering fundamentals to ship reliable systems

You are proactive, curious, and comfortable with a steep learning curve

You want to work on something technically hard that actually matters in the physical world

If you are early in your career but have strong fundamentals, a track record of self-directed learning, and a portfolio that shows you build things — we want to hear from you.

What You Will Work On

Multi-agent orchestration and LLM-driven triage workflows

Time-series modeling for anomaly detection, failure prediction, and health forecasting on multivariate telemetry

Retrieval-augmented knowledge systems for operations teams

Data and ML pipelines — ingestion, ETL, and dataset construction

Fine-tuning and post-training of language models for operational use cases

AI observability, evaluation frameworks, and production performance benchmarking

Responsibilities

Design, develop, and maintain AI-powered applications and automation workflows

Integrate and optimize LLM APIs for production use cases

Build and refine retrieval and knowledge-augmentation pipelines

Develop evaluation frameworks to benchmark AI system performance

Implement monitoring, tracing, and debugging capabilities for AI systems

Read and synthesize relevant research; bring ideas forward and debate them with the team

Contribute to AI architecture decisions and production hardening

Stay current with the rapidly evolving AI/ML landscape