Jobs›Machine Learning

Machine Learning Engineer

Egnyte · Work from home
Work from home
Pay₹22.5L–55La year, as listed
WhereWork from home
TypeFull time5-10 years
Posted28 Sep7 days ago, via the company site
Kaam checked
No fee, deposit or pay-to-apply signs
Day work
No vehicle or licence needed
Skills they list5 named
LoRA / QLoRAPythonSLMLarge Language Models (LLM)PyTorch
About this job

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With 23,000 customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career; you become part of a team of Egnyters who are doers, thinkers, and collaborators who embrace and live by our values

Invested Relationships

Fiscal Prudence

Candid Conversations

ABOUT EGNYTE

Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere.

WHAT YOU’LL DO

Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)

Optimize models for inference via 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 model quality using benchmarking frameworks and custom evaluation suites

YOUR QUALIFICATIONS

SLM Development & Fine-tuning
Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
Model Optimization
Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
Edge Deployment
Deploy models to edge devices, mobile, and local servers, etc.
Pipeline Engineering
Build end-to-end MLOps pipelines — from data ingestion to deployment.
Performance Monitoring
Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

Deployment experience on edge or mobile environments

Knowledge of ONNX export and cross-platform inference

MLOps tooling — experiment tracking, model registries, CI/CD for ML

EQUAL EMPLOYMENT OPPORTUNITY

At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.

Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.

Never pay to get work. If a listing asks for a fee, it is a scam. The ten signs →

Apply on the company site
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