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F-Secure - MLOps Engineer

F-SecureBangalore

30+ days ago

Job description

About The Role :

We are looking for Machine Learning Engineers to join our Technology team in Bengaluru! At F-Secure, we're building the next generation of AI-powered cybersecurity defenses that protect millions of users globally.

Our ML models operate in dynamic environments where threat actors constantly evolve their techniques.

We're looking for a motivated MLOps Engineer who has the foundational knowledge to help transition ML models from research to production systems.

This is an excellent opportunity to develop your skills in a real-world cybersecurity context with meaningful Technical Challenge :

Skills :

Youll grow your skills by working on exciting challenges that combine software engineering and machine learning as part of our internal team, the Scam Decipherers.

ML Pipeline Development : Contribute to building and maintaining ML pipelines that detect sophisticated threats in real-world environments
Threat Intelligence Systems : Help develop systems that analyse large volumes of online queries, behavioural events, and suspicious files
Model Monitoring : Implement frameworks for evaluating model performance and identifying when models need retraining
LLM Implementation : Gain hands-on experience deploying and optimizing LLMs for security applications like threat intelligence analysis and phishing detection

What Makes This Role Great For Growth :

This role at F-Secure offers unique advantages for developing your MLOps skills :

You'll work on real-world models that have actual impact on cybersecurity

You'll learn how ML systems operate under challenging conditions with real performance requirements

You'll collaborate with experienced data scientists and security researchers who can mentor your growth

You'll see the direct impact of your work on protecting users worldwide

You'll develop specialized knowledge in applying AI to cybersecurity - an increasingly valuable skill set

What are we looking for ?

ML Pipeline Knowledge : Understanding ML workflows, deployment challenges (through work, studies or personal projects), and basic knowledge on monitoring ML systems.

Cloud & Infrastructure Skills : Experience with cloud platforms through work, studies or personal projects (AWS, Azure, GCP), understanding of containerization concepts (Docker), and infrastructure as code approaches.

Model Evaluation Understanding : Knowledge of ML evaluation metrics, interpreting performance reports, and interest in learning how to detect and address model drift.

LLM Awareness : Basic understanding of LLMs, prompt engineering, and deployment in production.

Engineering Fundamentals : Solid Python skills, version control (Git), understanding of basic CI / CD concepts, and best practices for production code.

(ref : hirist.tech)

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Mlops Engineer • Bangalore