Senior Machine Learning Engineer – AI

🕒 August 18

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 35%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Egnyte

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2008

💼 Consulting

🏥 Healthcare

🏗️ Construction

💰 Private Equity Round - Egnyte on 2025-02

Consulting • Healthcare • Construction

Egnyte is an AI-powered content cloud and enterprise file-sharing platform that provides secure file storage, collaboration, and governance. It enables teams to access and co-edit large and complex files from the cloud via desktop and web apps, integrates with common productivity and cloud platforms, and adds AI features (assistant, agents, AI search, workflows) for insights and automation. Egnyte also includes built-in data governance, compliance automation, ransomware detection, and sensitive-data protection to meet enterprise regulatory needs.

📋 Description

• Fine-tune and train small language models using Hugging Face, TRL, and adapter methods • Optimize models for inference through 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 through deployment • Monitor model accuracy, latency, and hardware utilization in production • Evaluate model quality using benchmarking frameworks and custom evaluation suites

🎯 Requirements

• Experience training and fine-tuning SLMs using Hugging Face • Knowledge of adapters and adapter methods including LoRA, QLoRA, and PEFT • Experience applying quantization, pruning, and knowledge distillation for lightweight, efficient models • Experience deploying models to edge devices, mobile, and local servers • Experience building end-to-end MLOps pipelines from data ingestion to deployment • Experience monitoring model accuracy, latency, and CPU/GPU usage in production • Deployment experience in edge or mobile environments • Knowledge of ONNX export and cross-platform inference • Knowledge of MLOps tooling, including experiment tracking, model registries, and CI/CD for ML

🏖️ Benefits

• Equal employment opportunity and diversity, equity, and inclusion support through global Egnyte Employee Communities (EECs)