AI Engineer

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.

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.

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

Degree in Computer Science, Machine Learning, Mathematics, or a related field — or equivalent demonstrated experience

Strong proficiency in Python

Solid software engineering fundamentals: testing, version control, CI/CD

Experience working with LLM APIs in applied contexts

Familiarity with agentic system concepts — tool/function-calling, agent frameworks

Daily use of AI-assisted coding tools (Cursor, Copilot, Claude Code, etc.)

Ability to read ML research papers and translate ideas into practical experiments

Strong analytical thinking and clear communication — you can argue a position and update it when wrong

Professional AI/ML engineering experience (any level)

Experience building agentic systems using frameworks such as LangGraph or LangChain; MCP a plus

Time-series modeling — forecasting and anomaly/failure prediction on multivariate data

Experience fine-tuning or post-training language models

PyTorch and/or model serving frameworks (e.g., vLLM)

Experience building data and ML pipelines — ingestion, ETL, dataset construction

Familiarity with cloud ML platforms, particularly GCP (Vertex AI)

LLM evaluation and benchmarking: harness design and eval loop development

Domain experience with data center or industrial telemetry, BMS/OT protocols (Niagara, BACnet/Modbus)

Background in DevOps, distributed systems, or observability tooling