Senior AI/ML Engineer with Network Security

• Act to deliver.
• Disrupt to grow.
• Team up to win.

• The client is based in the US, with some of its employees located in India
• Close cooperation with the client’s representatives will be required
• Client’s AI/ML experts are already working on the project
• Online meetings in US-friendly hours (18:00, 19:00) will be required two to three times a week

• Develop an innovative cybersecurity product that leverages cutting-edge analytics, machine learning, and threat intelligence to provide advanced threat detection and incident response capabilities
• Preprocess network data, extract features, and build ML/DL models and pipelines for threat detection
• Evaluate ML/DL models’ efficiency, propose and implement changes to reduce false positive rates
• Develop scripts for continuous ML/DL monitoring and retraining/redeployment triggers
• Optimize ML/DL models to meet appropriate inference performance
• Explain how ML/DL models operate and why specific threats are flagged, ensuring model decisions are transparent and understandable
• Take part in technical discussions with the team and the client

• Strong grasp of core ML concepts (supervised/unsupervised learning, regularization, etc.). Hands-on experience with various classic ML models (classification, regression, clustering, anomaly detection) and with DL models utilizing various neural network architectures (CNNs, RNNs, LSTMs, Transformers, etc.)
• Deep experience with PyTorch for model development, training, evaluation, and deployment. Ability to write custom layers, loss functions, and use the PyTorch ecosystem
• Experience in network security, including a deep understanding of TCP/IP, DNS, HTTP, and TLS protocols, as well as flow and PCAP data analysis for threat detection
• Advanced Python programming skills for AI/ML model development and network security analysis, including experience with core and specialized libraries for efficient data processing, feature engineering, and scalable model deployment (PyTorch, Scikit-learn, Pandas/NumPy, Scapy, etc.)
• Familiarity with containerization and cloud deployment (AWS preferred)
• Ability to work both independently and in a team
• English at least B2 level, C1/C2 preferred

• Experience in AI models evaluation & monitoring: imbalanced classification know‑how, live metrics for drift/skew/quality, alert volume controls, build automated retraining/redeployment triggers
• Experience in behavioral modeling with network data: applying ML techniques to analyze and model behaviors in networked or graph-structured data, ability to extract features, detect patterns, and infer relationships or anomalies within large-scale, complex networks
• Skilled in leveraging threat intelligence and ML/DL techniques to analyze network traffic, detect malware operations, and identify suspicious activities such as connections to malware servers
• Experience with Explainable AI (XAI): knowledge of XAI techniques and tools (e.g., SHAP, LIME, Captum) to interpret and explain predictions of AI models
• Proven ability to design and implement models for anomaly detection using unsupervised learning techniques, such as clustering, autoencoders, and dimensionality reduction, to identify unusual patterns or outliers in large-scale datasets, crucial for detecting unknown threats or emerging malware behaviors

• Flexible working hours and approach to work: fully remotely, in the office or hybrid
• Professional growth supported by internal training sessions and a training budget
• Solid onboarding with a hands-on approach to give you an easy start
• A great atmosphere among professionals who are passionate about their work
• The ability to change the project you work on

CodiLime