Machine Learning Developer

Job Summary:

We are seeking a skilled and experienced Machine Learning Engineer with a strong focus on Cybersecurity to join our team. In this role, you will design, develop, and deploy machine learning models aimed at detecting anomalies, malicious activities, and security breaches. You will collaborate closely with cybersecurity teams to enhance security operations by leveraging advanced machine learning and AI techniques. Your expertise in data processing, feature engineering, and algorithm optimization will help drive continuous improvements in threat detection and prevention systems.

Key Responsibilities:

• Machine Learning Model Development:
• Design, develop, and deploy machine learning models for detecting anomalies, malicious activities, and security breaches.
• Utilize techniques like classification, clustering, anomaly detection, and reinforcement learning to create models for threat detection and intrusion prevention.
• Continuously improve and fine-tune models by analyzing large datasets from security logs, network traffic, and endpoint data.
• Cybersecurity Threat Intelligence:
• Collaborate with cybersecurity teams to understand current and emerging threat vectors, attack patterns, and techniques (e.g., malware, phishing, DDoS, APT).
• Develop systems that predict and identify new security threats using machine learning and AI.
• Integrate threat intelligence feeds into machine learning systems for real-time threat analysis and detection.
• Data Handling and Feature Engineering:
• Collect, preprocess, and analyze large volumes of security-related data (e.g., network logs, intrusion detection system logs, vulnerability data).
• Perform feature engineering and data transformation to extract meaningful features for training machine learning models.
• Ensure the integrity and privacy of sensitive data in compliance with data protection laws and industry standards.
• Algorithm Development and Optimization:
• Apply state-of-the-art algorithms (e.g., deep learning, NLP, time-series analysis) to enhance security operations.
• Optimize algorithms for low-latency processing in real-time threat detection systems.
• Collaboration and Cross-Functional Teams:
• Work with SOC teams, incident response teams, and cross-functional teams to integrate machine learning models into live security environments.
• Collaborate with data engineers, software engineers, and DevOps teams to deploy and maintain machine learning models in production.
• Model Evaluation and Performance Monitoring:
• Evaluate model performance using metrics such as precision, recall, F1-score, and ROC-AUC to ensure high accuracy and effectiveness in threat detection.
• Continuously monitor and refine models as new data and attack strategies emerge.
• Research and Innovation:
• Stay up to date with advancements in machine learning, cybersecurity, and AI, including emerging trends in attack techniques and defense mechanisms.
• Contribute to internal research on novel machine learning approaches and their applications to cybersecurity.
• Documentation and Reporting:
• Create detailed technical documentation for machine learning algorithms, data pipelines, and model deployments.
• Communicate findings, model results, and improvements to stakeholders, including executives, data scientists, and cybersecurity experts.
• Database Management:
• Manage and query Elasticsearch clusters for indexing, scaling, and searching large datasets.
• Use Logstash to ingest and process data from various sources.
• Create real-time visual dashboards and perform data analysis using Kibana.

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Skills & Qualifications:

Technical Skills:

• Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong knowledge of deep learning techniques (e.g., CNN, RNN, transformers) and their application to cybersecurity.
• Proficiency in programming languages such as Python and R.
• Expertise in data processing and visualization tools (e.g., Pandas, NumPy, Matplotlib).
• Familiarity with cybersecurity concepts, such as Advanced Persistent Threats (APT), Zero-Day Attacks, Brute Force Attacks, and Signature-less Malware Analysis.
• Experience with Big Data Platforms, including Hadoop and the ELK Stack (Elasticsearch, Logstash, Kibana).
• Proficiency in machine learning libraries like Apache Spark MLlib and H2O.ai for scalable machine learning and model deployment.
• Expertise in real-time data processing using Apache Kafka, Flink, and Apache Hive.

Soft Skills:

• Strong problem-solving skills, with the ability to analyze complex data and extract actionable insights.
• Excellent communication skills to explain complex machine learning concepts to non-technical stakeholders.
• Ability to work independently and manage multiple tasks in a fast-paced environment.
• Strong collaboration skills to work effectively within cross-functional teams.