Hire AI Inference Engineers
Erfan Miahi
Erfan trained in the RLAI Lab of Richard Sutton (Turing Award winner and a founding father of RL) and has collaborated with Google DeepMind. A reinforcement learning scientist and engineer with extensive experience, he owns LLM pre-training, post-training, and inference end-to-end. He co-authored Covenant-72B, history's largest decentralized LLM pre-training run, and created PULSE, now in Hugging Face TRL. His research has 260+ citations in venues such as EMNLP, AIJ, and TMLR.
Show MoreStevan Matovic
Stevan is an ML engineer with 7 years of experience building production AI systems across enterprise search, RAG, recommender systems, NLP, and multimodal AI. He specializes in taking ML products end to end, from experimentation and evaluation to scalable inference, deployment, and production integration. Stevan will be a great addition to any team.
Show MoreMaaz Khalid
Maaz is an AI infrastructure architect with 10+ years of experience with OpenStack, Ceph, and Kubernetes. He builds GPU platforms ranging from bare-metal to production LLM inference, including air-gapped deployments. Maaz designed AI Factory architectures for Core42 (G42) with NVIDIA and AMD GPUs on rail-optimized InfiniBand, and architected a sovereign AI Token Factory optimized with NVIDIA Dynamo and vLLM.
Show MoreHaris Mumtaz
Haris is a technology evangelist and senior technical lead with 11+ years of experience in driving innovation in AI, blockchain, and enterprise systems. He excels in leading cross-functional teams, aligning technology with business goals, and delivering high-impact solutions. Haris is recognized for fostering collaboration, mentoring talent, and transforming complex challenges into scalable outcomes, making him a vital asset in today's rapidly evolving tech landscape.
Show MoreAshish Shrivastava
Ashish has spent 12 years building deep learning and large language model solutions across technology and finance. His toolkit centers on AWS Fargate, Spark, and DynamoDB. While at TIFIN, Ashish reduced LLM inference costs by 70% and improved latency to under 3 seconds.
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