F-Secure - MLOps Engineer
1 day ago
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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