Senior MLOps Engineer
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Job Description
Senior MLOps Engineer deploying scalable ML services for Fortytwo’s decentralized AI protocol. Optimizing GPU infrastructure, model serving, and RAG systems for efficient swarm inference.
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 such as S3 for high availability and performance
- Integrate LoRA and model merging workflows into ML processes
- Work with and adapt state-of-the-art ML codebases to organizational needs
- Deploy and manage LLMs, SLMs, and LMMs
- Serve ML models using Triton Inference Server
- Use vLLM, TGI, and other state-of-the-art serving frameworks
- Optimize models with ONNX and TensorRT
- Develop RAG systems integrating spreadsheet, math, and compiler processors
- Set up monitoring and logging using Grafana, Prometheus, Loki, Elasticsearch, and OpenSearch
- Write and maintain CI/CD pipelines using GitHub Actions
- 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
- Preferred: Experience with advanced ML techniques, such as multi-sampling and dynamic temperatures
- Preferred: Knowledge of distributed training and large model fine-tuning
- Preferred: Proficiency in Go or Rust programming languages
- Preferred: Experience designing and implementing highly secure MLOps pipelines, including secure model deployment and data encryption
Benefits:
- Engage in meaningful AI research
- Work on decentralized inference, multi-agent systems, and efficient model deployment
- Build scalable and sustainable AI systems
- Collaborate with a highly technical team
- Autonomy in a research-driven environment
- Opportunity to work on foundational AI challenges
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