Join Teserac, Inc. to build AI-native applications and automation workflows for data center observability and workflow intelligence.

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 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

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, including tool/function-calling and 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, with the ability to argue a position and update it when incorrect

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

Build 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
Enable infrastructure owners to monitor, analyze, automate, and proactively manage power operations with full situational awareness
Develop an embedded AI teammate that acts as an always-on co-pilot for anomaly detection, event correlation, and recommendations 24/7
Work across the full AI lifecycle: data pipelines, model integration, agentic orchestration, evaluation, and production support
Research thinking matters, with expectations to read papers, stay ahead of the field, and turn ideas into production systems
Open to both junior and senior candidates

Professional AI/ML engineering experience (any level)
Experience building agentic systems using frameworks such as LangGraph or LangChain; MCP a plus
Time-series modeling experience, including forecasting and anomaly/failure prediction on multivariate data
Experience fine-tuning or post-training language models
PyTorch and/or model serving frameworks such as vLLM
Experience building data and ML pipelines: ingestion, ETL, dataset construction
Familiarity with cloud ML platforms, particularly GCP (Vertex AI)
LLM evaluation and benchmarking, including 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

AI Engineer

Job Description