AI Engineer-Cyber Security Transformation

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Engineer – Cyber Security Transformation based in India. As an AI Engineer specializing in cybersecurity transformation, you will design and deploy intelligent solutions that strengthen security operations and accelerate enterprise-wide AI adoption. You will develop AI-powered applications, copilots, and autonomous agents that help security teams improve threat detection, streamline workflows, and enhance operational efficiency. Working with large language models, retrieval-augmented generation, and modern AI architectures, you will transform enterprise knowledge and security data into actionable intelligence. You will also contribute to secure AI infrastructure, scalable model-serving platforms, and responsible AI implementation. In close collaboration with security operations, risk management, product security, vulnerability management, and threat intelligence teams, you will deliver solutions to complex cybersecurity challenges. This opportunity is ideal for an experienced engineer passionate about combining artificial intelligence, software engineering, and cybersecurity in a collaborative, innovation-driven environment.

Accountabilities

• Design, develop, and operationalize AI-powered applications, copilots, and autonomous agents tailored to cybersecurity use cases and enterprise security requirements.
• Build retrieval-augmented generation (RAG) solutions that leverage enterprise knowledge repositories, security data, and contextual information to improve the accuracy and relevance of AI-generated insights.
• Develop integrations using the Model Context Protocol (MCP) and APIs to connect AI agents with security tools, enterprise platforms, and operational workflows.
• Engineer secure, scalable, and maintainable AI services using modern software engineering, DevOps, and deployment practices.
• Enhance self-hosted AI infrastructure, model-serving environments, and AI observability capabilities to support reliable enterprise-scale deployments.
• Develop and automate security workflows that improve analyst productivity, reduce manual effort, and streamline cybersecurity operations.
• Collaborate with security operations, risk, product security, vulnerability management, and threat intelligence teams to identify business needs and deliver practical AI-driven solutions.
• Implement and optimize agentic workflows and multi-agent systems to support complex security tasks and improve operational decision-making.
• Apply secure AI design principles to mitigate risks such as prompt injection, model misuse, sensitive data leakage, and unauthorized agent actions.
• Establish and maintain engineering standards for CI/CD, version control, automated testing, code quality, monitoring, and production readiness.
• Troubleshoot technical issues, evaluate emerging AI technologies, and continuously improve the performance, security, scalability, and reliability of AI solutions.

Requirements

• A bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Software Engineering, Cybersecurity, Data Science, or a related technical discipline.
• At least five years of professional experience in software engineering, AI engineering, machine learning engineering, platform engineering, or a closely related field.
• Strong proficiency in Python and experience designing and developing API-driven architectures and production-grade AI applications.
• Hands-on experience applying large language models (LLMs), AI agents, prompt engineering, retrieval-augmented generation (RAG), vector databases, and semantic search.
• Demonstrated ability to build, deploy, and maintain AI-powered applications and services in production environments.
• Solid understanding of software engineering and DevOps practices, including CI/CD pipelines, version control, automated testing, and code quality management.
• Experience with self-hosted LLM environments, enterprise AI platforms, and model deployment or serving infrastructure.
• Practical knowledge of agentic workflows and multi-agent architectures, including the integration of AI agents with external tools and systems.
• Understanding of AI security risks, including prompt injection, model abuse, data leakage, and secure agent design, with the ability to implement appropriate safeguards.
• Strong analytical thinking, troubleshooting, and prob…