OT Cyber Security Consultant securing operational technology environments with a focus on ICS and SCADA networks. Developing cybersecurity strategies and collaborating with clients across various sectors.
About the role
- Security Data Scientist / ML Engineer deploying machine learning systems for AI-driven security solutions at Salesforce. Collaborating with teams to design and operationalize models that enhance real-time threat detection.
Responsibilities
- Build and deploy intelligent, data-driven systems that utilize machine learning and AI agents to enable real-time attack pattern identification, risk assessment, and proactive defense across Salesforce products and global infrastructure.
- Lead the integration of Large Language Models (LLMs) and autonomous agents with security data pipelines.
- Oversee the end-to-end lifecycle—including development, release, monitoring, and operation—of ML models and AI agents to ensure they deliver high-impact protection at scale.
- Develop autonomous AI agents capable of executing complex security tasks, such as performing advanced data correlations, identifying specific attack patterns, conducting automated penetration testing, and analyzing security impacts.
- Engineer platforms that synthesize large-scale security telemetry into actionable risk intelligence and automated decisions.
- Partner closely with security engineers, infrastructure teams, and product stakeholders to deeply understand the threat landscape and design solutions that address high-priority security problems.
- Apply LLMs and prompt engineering to automate generation of security insights, explanations, and response workflows from detections and anomalies.
- Continuously improve algorithmic performance with a focus on detection, classification, and behavioral modeling in security and threat intelligence domains.
- Collaborate effectively with team members and suggest improvements to reduce time-to-detection and mature our security ML platform.
Requirements
- 6+ years of industry experience with a demonstrated passion for crafting, analyzing, and deploying scalable machine learning solutions.
- Proven track record in machine learning engineering focused on security use cases, such as anomaly detection, malware classification, or behavioral modeling.
- Experience deploying, monitoring, and maintaining ML systems in cloud environments (specifically AWS or GCP).
- Consistent record of building ML products using modern lifecycle methodologies, including CI/CD, QA, and Agile practices.
- Expert-level proficiency in writing high-quality, well-documented, and tested code, with a strong preference for Python.
- Solid understanding of data transformations and analytics using libraries and languages such as Pandas, Scikit-learn, SQL, and Spark.
- Hands-on experience with standard ML and orchestration tools like mlFlow, Airflow, and Docker.
- Strong understanding of Statistics and Machine Learning methods, including the challenges associated with the end-to-end ML project lifecycle.
Benefits
- time off programs
- medical
- dental
- vision
- mental health support
- paid parental leave
- life and disability insurance
- 401(k)
- employee stock purchasing program
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