This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer based in India.
This role offers the opportunity to build production-grade machine learning systems that address complex, rapidly evolving security challenges.
You will design, train, fine-tune, and deploy models that power intelligent threat detection at significant scale.
A key focus will be developing models that are lightweight, fast, reliable, and cost-efficient in high-throughput environments.
You will collaborate closely with security researchers, platform engineers, and product teams to turn advanced ML techniques into practical capabilities.
The role combines applied AI, model optimization, data pipelines, software engineering, and production infrastructure.
You will continuously evaluate models across accuracy, latency, memory usage, inference cost, and operational reliability.




This is an ideal chance for an engineer who enjoys taking machine learning from experimentation to robust production systems with real-world impact.
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Accountabilities
Design, train, fine-tune, and evaluate machine learning models for security detection use cases.
Build and deploy lightweight, high-performance models optimized for low latency, high throughput, low inference cost, and operational reliability.
Develop and maintain fine-tuning pipelines for large language models and smaller transformer-based architectures.
Experiment with advanced model optimization techniques, including knowledge distillation, quantization, pruning, retrieval-augmented generation, and parameter-efficient fine-tuning approaches such as LoRA and adapters.
Improve detection quality while balancing false positives and false negatives to deliver accurate and reliable security outcomes.
Build scalable machine learning infrastructure and production-grade inference pipelines.
Partner with security researchers to translate detection logic and researc

📌 Machine Learning Engineer (India)
🏢 Jobgether
📍 India