AI/ML Security Architect and Engineer

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities

We are seeking a highly skilled and motivated AI Security Consultant/Engineer to join our growing security team. In this role, you will design, develop, and deploy AI-driven solutions to detect, prevent, and respond to cyber threats and be responsible for securing AI/ML systems, models, and data pipelines against adversarial threats, ensuring compliance with security best practices, and collaborating with cross-functional teams to build robust, trustworthy AI solutions. You will work at the intersection of machine learning, data science, and security engineering to build intelligent systems that enhance our security posture.

Required Technical And Professional Expertise

• Architect and implement scalable Generative AI features, including agentic workflows, conversational AI, and autonomous agents.
• Develop and deploy machine learning models for threat detection, anomaly detection, malware classification, and behavioural analysis.
• Apply best practices in AI security, including mitigation of hallucinations, prompt injection, and bias.
• Identify and mitigate risks related to adversarial machine learning, model inversion, data poisoning, and prompt injection.
• Analyse large-scale security datasets (e.g., logs, network traffic, endpoint telemetry) to identify patterns and build predictive models.
• Research and implement AI and Machine Learning techniques to improve detection accuracy and reduce false positives.
• Design and implement security controls for AI/ML systems, including model training, inference, and data pipelines.
• Collaborate with security analysts and incident response teams to integrate AI tools into existing workflows.
• Collaborate with data scientists, ML engineers, and DevOps teams to integrate security into the AI/ML lifecycle.
• Build automation pipelines for data preprocessing, model training, evaluation, and deployment.
• Monitor model performance and retrain models as needed to adapt to evolving threats.
• Stay current with emerging threats, vulnerabilities, and research in AI security and adversarial machine learning.
• Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) and AI governance frameworks.
• Ensure AI systems are explainable, auditable, and aligned with ethical and regulatory standards.
• Experience in full AI project lifecycle, from research and prototyping to deployment in production environments.
• Familiarity with Agile development methodologies
• Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, or similar.
• Experience with AI and/or data governance
• Experience with building automation solutions with AI/ML.
• Experience with Ansible, Red Hat OpenShift, Kubernates and CI/CD Pipelines.
• Experience with secure MLOps practices and tools (e.g., MLflow, Kubeflow, SageMaker).
• Experience with LangChain, OpenAI APIs, or similar LLM frameworks (highly desirable).
• Knowledge of RAG (Retrieval-Augmented Generation), vector databases, and custom embeddings.
• Experience with vector DB’s or open file formats like parquet, avro or orc
• Strong understanding of cybersecurity principles, threat landscapes, and common attack vectors.
• Experience with threat modeling and securing cloud-based AI infrastructure (e.g., AWS, Azure, GCP).
• Experience with data engineering and working with large-scale datasets.
• Familiarity with cloud platforms (AWS, Azure, GCP) and security tools (SIEM, EDR, IDS/IPS).
• Excellent problem-solving and communication skills.
• Knowledge of AI ethics, fairness, and explainability.