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 impact.

The 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.