Machine Learning Engineer

Jobgether; India; On-site; INR 600,000 - 900,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive employee benefits

Flexible work environment

Flexible time-off programs

Well-being days

Volunteer days

Professional development

Job summary

Jobgether is seeking a Machine Learning Engineer based in India to build production-grade ML systems addressing complex security challenges. You will design, train, fine-tune, and deploy models powering intelligent threat detection at scale.

A core focus is creating lightweight, fast, and cost-efficient models for high-throughput environments while collaborating with security researchers, platform engineers, and product teams to translate research into practical capabilities.

Qualifications

• 2+ years of experience in Machine Learning Engineering or related field.
• Experience deploying ML systems in production environments.
• Hands-on with fine-tuning transformer-based models / LLMs.
• Strong Python programming and software engineering skills.
• Experience with PyTorch and Hugging Face; TensorFlow a bonus.

Responsibilities

• Design, train, fine-tune, and evaluate ML models for security use cases.
• Build lightweight, high-performance models with low latency and cost.
• Develop fine-tuning pipelines for large language models and transformers.
• Experiment with optimization techniques like distillation, quantization, pruning, and LoRA.

Skills

Python programming

ML engineering

Transformer models

Distributed training

Problem solving

Tools

PyTorch

Hugging Face

TensorFlow

Job description

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 opportunity for an engineer who enjoys taking machine learning from experimentation to robust production systems with real-world impact.

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 research concepts into ML-powered production systems.
• Measure model performance across key dimensions including quality, inference speed, memory footprint, scalability, and cost.
• Contribute to data engineering, dataset preparation, and labeling workflows supporting supervised machine learning.
• Monitor deployed models and identify opportunities to improve robustness, reliability, and performance over time.
• Develop experimentation and evaluation processes that enable informed decisions about model performance and production readiness.
• Collaborate with platform and product engineering teams to integrate ML capabilities into scalable production environments.
• Apply strong software engineering practices throughout model development, deployment, monitoring, and maintenance.

Requirements

• At least 2 years of experience in Machine Learning Engineering, Applied AI, or a closely related field.
• Demonstrated experience building, deploying, and maintaining machine learning systems in production environments.
• Hands-on experience fine-tuning transformer-based models and/or large language models.
• Strong Python programming and software engineering skills.
• Experience with modern machine learning frameworks such as PyTorch and Hugging Face; TensorFlow experience is an advantage.
• Practical knowledge of model optimization techniques and experience improving inference efficiency at scale.
• Solid understanding of machine learning model evaluation, experimentation, and performance measurement.
• Experience working with data pipelines and datasets used for supervised machine learning.
• Understanding of distributed training and scalable machine learning infrastructure.
• Experience deploying ML models in cloud-based or containerized environments.
• Strong understanding of production software engineering principles, including reliability, scalability, maintainability, and monitoring.
• Analytical and problem-solving mindset, with the ability to investigate complex technical challenges and turn experimentation into practical solutions.
• Ability to collaborate effectively with security researchers, engineers, product teams, and other technical stakeholders.
• Comfortable working in an environment where priorities evolve quickly and continuous experimentation and improvement are encouraged.
• Strong ownership, curiosity, attention to detail, and commitment to delivering reliable production systems.

Benefits

• Competitive compensation.
• Comprehensive employee benefits.
• Flexible work environment.
• Flexible time-off programs.
• Well-being initiatives, including paid wellbeing days.
• Paid volunteer/community outreach days.
• Opportunities for professional development and long-term career growth.
• Global collaboration and networking opportunities with multidisciplinary teams.
• Opportunity to work on advanced machine learning and AI applications with real-world cybersecurity impact.
• Exposure to modern ML technologies, including LLMs, transformers, model optimization, and scalable inference.
• Opportunity to collaborate with experienced machine learning, security research, platform, and product professionals.
• Three-week Work from Anywhere option, subject to applicable policies and eligibility.
• Additional benefits and perks may vary based on location and applicable employment policies.