Fortytwo - MLOps Engineer
Fortytwo is a decentralized AI protocol on Monad that leverages idle consumer hardware for swarm inference. It enables Small Language Models to achieve advanced multi-step reasoning at lower costs, surpassing the performance and scalability of leading models.
Responsibilities
- Deploy scalable, production-ready ML services with optimized infrastructure and auto-scaling Kubernetes clusters
- Optimize GPU resources using MIG (Multi-Instance GPU) and NOS (Node Offloading System)
- Manage cloud storage (e.g., S3) to ensure high availability and performance
- Integrate state-of-the-art ML techniques, such as LoRA and model merging, into workflows:
- Work with SOTA ML codebases and adapt them to organizational needs
- Integrate LoRA (Low-Rank Adaptation) techniques and model merging workflows
- Deploy and manage large language models (LLM), small language models (SLM), and large multimodal models (LMM)
- Serve ML models using technologies like Triton Inference Server
- Leverage solutions such as vLLM, TGI (Text Generation Inference), and other state-of-the-art serving frameworks
- Optimize models with ONNX and TensorRT for efficient deployment
- Develop Retrieval-Augmented Generation (RAG) systems integrating spreadsheet, math, and compiler processors
- Set up monitoring and logging solutions using Grafana, Prometheus, Loki, Elasticsearch, and OpenSearch
- Write and maintain CI/CD pipelines using GitHub Actions for seamless deployment processes
- Create Helm templates for rapid Kubernetes node deployment
- Automate workflows using cron jobs and Airflow DAGs
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- Proficiency in Kubernetes, Helm, and containerization technologies
- Experience with GPU optimization (MIG, NOS) and cloud platforms (AWS, GCP, Azure)
- Strong knowledge of monitoring tools (Grafana, Prometheus) and scripting languages (Python, Bash)
- Hands-on experience with CI/CD tools and workflow management systems
- Familiarity with Triton Inference Server, ONNX, and TensorRT for model serving and optimization
Preferred
- 5+ years of experience in MLOps or ML engineering roles
- Experience with advanced ML techniques, such as multi-sampling and dynamic temperatures
- Knowledge of distributed training and large model fine-tuning
- Proficiency in Go or Rust programming languages
- Experience designing and implementing highly secure MLOps pipelines, including secure model deployment and data encryption
Why Work with Us
At Fortytwo, we are building a research-driven, decentralized AI infrastructure that prioritizes scalability, efficiency, and sustainability. Our approach moves beyond centralized AI constraints, applying globally scalable swarm intelligence to enhance LLM reasoning and problem-solving capabilities.
- Engage in meaningful AI research – Work on decentralized inference, multi-agent systems, and efficient model deployment with a team that values rigorous, first-principles thinking
- Build scalable and sustainable AI – Design AI systems that reduce reliance on massive compute clusters, making advanced models more efficient, accessible, and cost-effective
- Collaborate with a highly technical team – Join engineers and researchers who are deeply experienced, intellectually curious, and motivated by solving hard problems
We’re looking for individuals who thrive in research-driven environments, value autonomy, and want to work on foundational AI challenges.
