Machine Learning Engineer - AI

Egnyte
India
Workplace: RemoteFull timeFunction: Data Science & Machine LearningSkills: []

Fine-tune and train small language models using Hugging Face, TRL, and adapter methods like LoRA/QLoRA/PEFT. Optimize models for efficient inference through quantization, pruning, and knowledge distillation, and deploy them to edge, mobile, and local server environments with strict latency targets. Build end-to-end MLOps pipelines, monitor model accuracy/latency/CPU-GPU utilization in production, and evaluate quality via benchmarking and custom evaluation suites.

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FursaFursa
Egnyte
Egnyte
2 weeks ago

Machine Learning Engineer - AI

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Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Fine-tune and train small language models using Hugging Face, TRL, and adapter methods like LoRA/QLoRA/PEFT. Optimize models for efficient inference through quantization, pruning, and knowledge distillation, and deploy them to edge, mobile, and local server environments with strict latency targets. Build end-to-end MLOps pipelines, monitor model accuracy/latency/CPU-GPU utilization in production, and evaluate quality via benchmarking and custom evaluation suites.
Location: India
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT).
  • •Optimize models for inference using quantization, pruning, and knowledge distillation.
  • •Deploy models to edge devices, mobile, and local servers with strict latency targets.
  • •Build end-to-end MLOps pipelines from data ingestion to deployment.
  • •Monitor model accuracy, latency, and hardware utilization in production; evaluate quality with benchmarking and custom evaluation suites.

Key Requirements

  • •Train and fine-tune SLMs using Hugging Face and adapter methods (Knowledge on Adaptors).
  • •Optimize models for lightweight, efficient inference using quantization, pruning, and knowledge distillation.
  • •Deploy models to edge devices, mobile, and local servers with strict latency targets.
  • •Build end-to-end MLOps pipelines from data ingestion to deployment.
  • •Monitor model accuracy, latency, and CPU/GPU usage in production; evaluate model quality with benchmarking and custom evaluation suites.
Tech Stack:Hugging FaceTRLLoRAQLoRAPEFTQuantizationPruningKnowledge distillationMLOps pipelinesExperiment trackingModel registriesCI/CDONNXEdge devicesMobileLocal serversCPU/GPU

Company Brief

Egnyte
Provides a cloud-based content governance and secure file-sharing platform enabling businesses to manage, protect, and collaborate on files across cloud and on-premises systems with compliance, data protection, and access controls.
Industry: SaaS
Company Size: Large (251 to 1,000 employees)
Growth: Established Company
Headquarters: Mountain View, United States
Founded: 2007
WebsiteLinkedIn