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# Senior AI Engineer

- **Company:** Rise Technical Recruitment Ltd
- **Location:** Boston, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $180,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Performance Tuning, Pytorch, Large Language Models, Multi-Agent Systems, Kubernetes, HuggingFace, Machine Learning Operations, TensorRT, Data Generation
- **Published:** June 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ea06fa138027f3b6

## About the Role

Do you have experience in System performance optimization?, * Experience building production ML systems, with a strong focus on LLMs or LLM-powered agents

 * Strong Python programming alongside PyTorch and Hugging Face
 * Experience with Supervised Fine-Tuning, preference optimization, and synthetic data generation
 * Experience shipping multi-agent or tool-using LLM systems to production
 * US Citizen

## Description

Are you a Senior AI Engineer with experience building production-grade LLM systems looking to take ownership of autonomous, multi-agent architectures within a high-growth cybersecurity AI start-up?

This is an opportunity to join a fast-growing AI company developing next-generation autonomous offensive security platforms. You will join a specialized AI engineering team and play a critical role in advancing their flagship platform, moving the needle on complex problems like exploit chaining, multimodal vision, and co-evolutionary self-training loops.

In this role, you will design, implement, and optimize highly sophisticated multi-agent architecture, while contributing directly to model fine-tuning and production scalability. Working closely with engineering leadership and cross-functional teams, you will transition advanced multi-agent systems from cutting-edge experimentation into robust, production-ready enterprise solutions.

This role would suit an AI Engineer who enjoys solving deep, technical agent-orchestration challenges and wants to be a foundational builder of autonomous, self-improving security systems., * Design and improve multi-agent architectures, planning loops, and tool-use

 * Contribute to model training via data curation, Supervised Fine-Tuning, and preference optimization (DPO/GRPO)
 * Build evaluation harnesses to track model and agent performance
 * Optimize inference for latency and throughput using vLLM or TensorRT-LLM
 * Deploy and scale production agents within Kubernetes environments


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