TechGenies
AI Engineer
TechGeniesOman1 day ago
Full-timeRemote FriendlyInformation Technology
This is a remote position.

AI Engineer

Cybersecurity Analytics Platform

About The Role

We are looking for a motivated AI Engineer to design, build, and deploy artificial intelligence capabilities within Octopus VAR, our cybersecurity configuration review and analytics platform.

This is a hands on, product focused engineering role, not a research only position. You will work with real operational security data and help ship AI driven features that support security teams in detecting risks, prioritizing actions, and making informed decisions.

You will collaborate closely with full stack developers and cybersecurity engineers to translate real world security challenges into scalable, explainable, and reliable AI powered solutions.

What You Will Work On

You will apply machine learning and applied AI techniques to operational security data, including logs, configurations, compliance benchmarks, and behavioral patterns.

Your work will focus on production ready AI, embedded directly into the product and used by real security teams.

Key Responsibilities

AI and Machine Learning Development

  • Design and implement machine learning models for:
  • Anomaly detection in logs and system configurations
  • Risk scoring and prioritization
  • Compliance deviation and drift analysis
  • Predictive security insights
  • Apply supervised, unsupervised, and semi supervised learning techniques as appropriate.
  • Build and optimize NLP and AI driven capabilities for:
  • Log parsing and normalization
  • Automated technical and executive level reporting
  • AI driven explanations, summaries, and insights

Data Engineering and ML Pipelines

  • Design and maintain data pipelines to ingest:
  • System and application logs
  • Infrastructure and security configuration files
  • Compliance benchmark outputs such as CIS and vendor best practices
  • Clean, normalize, and structure structured and unstructured security datasets.
  • Develop and maintain ML pipelines covering data preparation, training, evaluation, and deployment.
  • Use Jupyter notebooks for experimentation, then transition models into production pipelines.

AI Integration and Productization

  • Integrate AI models into backend services using Python and Node.js.
  • Expose AI functionality through APIs for frontend applications and reporting engines.
  • Optimize models for performance, explainability, and operational reliability.
  • Ensure AI outputs are actionable and understandable for security professionals.

Security Aware AI Development

  • Apply secure coding practices across AI pipelines.
  • Understand adversarial considerations such as false positives, evasion, and data poisoning.
  • Collaborate with cybersecurity engineers to align AI outputs with real threat models and operational needs.

Continuous Improvement

  • Monitor model performance and retrain models as data evolves.
  • Evaluate emerging AI tools, frameworks, and techniques relevant to cybersecurity.
  • Document models, assumptions, and limitations clearly for internal teams.

Why Join Us

  • Work on real world cybersecurity problems with immediate product impact.
  • High ownership role with direct influence on AI capabilities and product evolution.
  • Opportunity to build and ship AI features early in a growing cybersecurity platform.
  • Collaborative engineering environment focused on practical, usable AI.

Final Note

This role is ideal for an AI Engineer who enjoys building production systems, working close to the product, and solving applied problems using real data. If you prefer shipping value over academic experimentation, this role is designed for you.

Requirements

What We Are Looking For

Required Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or equivalent practical experience.
  • 3 plus years of hands on experience applying AI and machine learning in real world, production systems.
  • Strong proficiency in Python for machine learning and data processing.
  • Hands on experience with machine learning frameworks such as scikit learn, PyTorch, TensorFlow, or similar.
  • Experience working with Jupyter notebooks and transitioning experiments into production ready ML pipelines.
  • Experience integrating ML models into backend systems and APIs.
  • Ability to work with structured and unstructured data at scale.

Nice to Have

  • Experience in cybersecurity, DevOps, infrastructure analytics, or observability platforms.
  • Familiarity with security data such as logs, configurations, or compliance outputs.
  • Exposure to compliance frameworks such as CIS benchmarks.
  • Experience with AI assisted reporting, summarization, or insight generation.
  • Familiarity with cloud platforms such as AWS, Azure, or GCP.
  • Exposure to model monitoring, retraining, or basic MLOps practices.

Key Skills

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