Published on
Full-Time
Remote, Hybrid, or On-Site
Salary Negotiable
About the Role
Senior AI Engineer
NetSpeek's Lena is the first Language Enabled Network Administrator built exclusively for operating multi-vendor networks of UC and Pro AV technologies. As a generative AI platform built from the ground up for enterprise-scale network operation, Lena automates much of the day-to-day administration of your UC and Pro AV network. Lena is a multi-lingual network administrator trained on a wealth of industry standards, platform-specific training, technical documentation and the operational history and context specific to your enterprise. Lena can autonomously monitor the network 24x7x365, observe when issues arise, investigate root causes, develop and execute resolutions, and generate reports to optimize both autonomous and human-driven workflows.
Overview
We are not looking for a backend engineer who "added AI." We are not looking for someone whose LLM experience is internal tooling and a side project.
We are looking for an ML-leaning engineer who has built, shipped, and stayed on call for AI systems inside a growth-phase AI SaaS company. Somewhere AI was the product, not a feature added later.
You think like an ML engineer first. The model call is the easy part. The interesting part is everything that wraps it.
Key Responsibilities
Design and evolution of agentic reasoning flows inside the control plane
Retrieval quality, embedding strategy, grounding discipline
Evaluation pipelines and the metrics they hold us to
Hallucination detection and how we react to it in real time
Structured outputs that downstream services can actually trust
Human-in-the-loop escalation logic — when Lena should not act
Cost and latency optimization under real production traffic
Model selection and the discipline around when to switch
You will work directly with the AI Team Lead, the EVP Product and Engineering, the backend engineers who own action execution, and the UX team that shapes how operators see Lena. This seat shapes how intelligence behaves in the platform.
Technical Requirements
5+ years of ML engineering, or a combined 5+ years across ML and applied AI
2+ years building and shipping LLM-powered systems inside a growth-phase AI SaaS company
Hands-on RAG ownership: vector databases, embedding tuning, retrieval optimization, grounding strategy, and the failure modes of each
Built evaluation pipelines for LLM performance and reliability that measured something real and changed a decision
Strong Python on production systems, not on notebooks
Comfort operating under live constraints: latency, cost, observability, safety
If your LLM experience is experimentation, side projects, or non-production work, this is not the right level. Honest answer: take a year at this level somewhere else first, then come back. We will still be here.
Preferred Qualifications
Designed agentic workflows that measurably improved on a baseline
Came from an AI-native company that scaled from early traction into growth
Reduced hallucination or improved grounding in production, with the numbers to show it
Cost optimization at scale — caching, prompt redesign, retrieval rework
Built or operated AI logging that an enterprise security team would sign off on
Soft Skills
Proactive: Take initiative and drive projects forward while collaborating effectively with the team.
Continuous Learner: Passionate about staying current with emerging AI technologies.
Problem Solver: Brings an innovative approach to complex technical challenges
Featured Benefits
Medical insurance
Requirements Added by the Job Poster
• Authorized to work in the United States
