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# ML Engineer

- **Company:** Linero, Inc.
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Information Extraction, Machine Learning, Datadog, QLoRA, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, IT Architecture, Agentic-AI, Document Classification, Web Api
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_5ede25bf78e380cbde11df2022d7dcf1

## About the Role

* 4+ years of experience in AI/ML engineering with a strong emphasis on production systems
 * Deep experience fine-tuning and evaluating large language models for domain-specific applications; you know how to get reliable, accurate results from existing models, not just benchmark performance
 * Strong experience building RAG systems, retrieval architecture, chunking strategies, embedding models, and evaluation
 * Experience designing and deploying multi-agent systems: orchestration, tool use, failure modes, and observability
 * Open-Weight Model Adaptation: Hands-on experience fine-tuning (LoRA/QLoRA, full fine-tuning) and serving open-weight models locally, with a deep understanding of optimizing inference latency, throughput, and memory constraints in air-gapped or restricted infrastructure without relying on external APIs.
 * Strong software engineering fundamentals; you build and ship production systems, not just proofs of concept
 * Experience with information extraction, document classification, or NLP at scale is a strong plus
 * Based in or willing to relocate to New York City

## Description

As an ML Engineer, you will build and own the intelligence layer at the core of our product: models, pipelines, and agent systems that allow Linero to reason over complex, sensitive enterprise data, entirely within controlled, private environments., * Design and own Linero's AI architecture, selecting, fine-tuning, and deploying the right models for each use case within controlled, private environments

 * Build and maintain RAG pipelines that allow models to reason over sensitive, proprietary enterprise data without exposing it to public APIs
 * Fine-tune foundation models on domain-specific data to achieve the classification accuracy and reliability our customers require
 * Design and orchestrate a fleet of specialized AI agents, including agents that process and tag internal enterprise documents and agents that monitor and analyze external data sources
 * Build evaluation frameworks, benchmarks, and observability tooling to ensure model reliability, accuracy, and auditability in production
 * Own the private inference infrastructure, ensuring all AI runs in controlled environments that meet the security posture of our most demanding customers
 * Work closely with platform and infrastructure teams to ensure AI systems are integrated securely and can scale across the product