Senior AI Cybersecurity Consultant

Introduction

We are seeking a highly skilled and forward-thinking AI Cybersecurity Consultant to lead the integration of Artificial Intelligence and Large Language Models (LLMs) into advanced cybersecurity solutions. This role combines strategic advisory, technical design, and hands-on implementation to enhance threat detection, response automation, and intelligence capabilities across client environments. The ideal candidate will have deep expertise in cybersecurity, AI / ML, and LLM frameworks, with a passion for innovation and stakeholder enablement.

Your role and responsibilities

Strategic Advisory

Assess client security architectures, SOC workflows, and threat management processes to identify opportunities for AI and LLM integration. Provide Subject Matter Expertise on Generative AI adoption and Impactful deployment for cybersecurity for internal architecture, third party integration, and custom models.

Perform Market Research to identify new opportunities, ideas and technology trends for adoption. Collaborate with Vendors and Industry leaders to accelerate adoption of latest technology trends and ideas that have strong business impact

Develop AI adoption roadmaps aligned with business, security, and compliance objectives.

Advise on AI-driven security architecture, including SIEM / SOAR platforms, threat intelligence, threat hunting and endpoint protection.

Solution Design & Implementation

Design and deploy ML models for advanced threat detection, incident triage, including anomaly detection, , phishing detection, data leak prevention and behavioral analytics.

Build and integrate LLM-based security agents for automated alert triage, incident enrichment, and adversary simulation using IBM WatsonX and agnostic frameworks (e.g., Langgraph , Other LLMs).

Customize models to meet specific threat landscapes and regulatory requirements.

Data Engineering & Model Lifecycle

Collaborate with data scientists and engineers to curate security telemetry, threat intelligence feeds, and log data for model training.

Apply MLOps / LLMOps practices for model deployment, monitoring, and retraining.

Ensure data privacy and compliance throughout the AI / ML lifecycle.

Threat Intelligence & Automation

Enhance threat hunting, vulnerability prioritization, and predictive analytics using AI and LLMs.

Automate SOC workflow such as alert triage, incident classification, and playbook execution.

Develop and maintain AI-powered threat intelligence feeds.

Governance & Risk Management

Conduct risk assessments for AI / LLM models, addressing bias, adversarial robustness, and model drift.

Ensure compliance with standards such as GDPR, NIST, and ISO 27001.

Establish governance frameworks for ethical AI use in cybersecurity.

Stakeholder Engagement & Enablement

Educate clients and internal teams on AI / LLM capabilities and limitations.

Lead workshops, demos, and training sessions to build AI literacy.

Collaborate with vendors and partners to evaluate third-party AI / LLM security solutions.

This Job can be Performed from anywhere in the US.

Required technical and professional expertise

Core Competencies

Strong understanding of cybersecurity principles, threat landscapes, and security frameworks.

Proven experience in AI / ML development for security use cases, including NLP and LLM applications for threat detection and SOC automation.

Familiarity with threat hunting, incident response, and security data transformation.

Technical Expertise

Proficiency in Python (required); C# (desirable).

Experience with AI / ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn, Kubeflow).

Working knowledge in LLM models like Gemini, Llama, GPT, DALL-E and frameworks like LangChain & Vector databases like Faiss, Pinecone etc.

Understanding of RAG architectures, vector databases, and prompt engineering.

Familiarity with LangGraph , REACT frameworks and agent-based LLM design for SOC automation.

Operational Knowledge

Hands-on experience with security platforms (e.g., Splunk, XSIAM, Sentinel, Cortex XSOAR) and threat intelligence tools.

Familiarity with cloud environments (AWS, Azure, GCP) and their security services.

Knowledge of MLOps / LLMOps tools and practices for model lifecycle management.

Soft Skills

Excellent communication and stakeholder engagement skills.

Ability to translate complex AI / ML concepts into actionable cybersecurity strategies.

Preferred technical and professional experience

• Experience with IBM WatsonX, Open AI, CoPilot or similar enterprise AI / LLM platforms (preferred).

IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

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