Head of AI Engineering & Cyber Solutions

Primary Expectations The primary expectation is to hire a strong IT development and software engineering leader with proven AI delivery experience, who also understands cybersecurity sufficiently to build relevant, secure, and effective solutions for cyber teams.

Key Responsibilities

1. Lead AI Engineering & Solution Delivery

• Define and execute the AI strategy across security, risk, and cyber enablement

• Build and scale the AI Hub in India as a global centre of excellence

• Own the design, development, and delivery of AI-enabled platforms, tools, and applications for cybersecurity teams.

• Build robust software engineering practices across architecture, development, testing, release management, DevSecOps, and production support.

• Lead engineering teams across AI engineers, software developers, data engineers, and automation specialists.

• Translate business and cyber requirements into scalable, maintainable, production-grade technology solutions.

• Progressively take ownership of broader IT development delivery for the team, beyond AI-only use cases.

• Partner with global security, data, and technology leadership on AI initiatives

1. Software Engineering & Technology Delivery

• Strong foundation in software architecture, application development, APIs, microservices, cloud-native engineering, and enterprise integration.

• Experience with modern engineering practices including Agile delivery, CI/CD, DevSecOps, automated testing, observability, secure coding, and production support.

• Ability to lead end-to-end solution delivery from problem framing and design to build, deployment, adoption, and run.

• Comfortable managing developers, AI engineers, data engineers, architects, and technical leads.

1. AI for Cyber (AI Enablement & Use Cases)

• Drive development and scaling of AI use cases for cybersecurity, including:

  • Security operations, threat detection, vulnerability management, AppSec, automation, reporting

• Translate cybersecurity problems into AI-driven solutions

• Work closely with AI engineers, data scientists, and cyber teams to deliver practical outcomes

• Ensure use cases are secure, scalable, and production-ready

1. Cyber for AI (AI Security & Risk)

• Establish and drive security practices for:

  • AI/ML pipelines, GenAI / LLM systems, and AI-enabled platforms and applications
  • Azure / AWS AI components and all relevant mitigations (esp guardrails)
  • Microsoft agentic ecosystem (M365 copilot, studio, github)
  • Data/ AI converged platforms (eg Databricks, Snowflake)

• Oversee cybersecurity and risk assessment of AI use cases across the lifecycle

• Define controls, standards, and frameworks for secure AI adoption

• Ensure alignment with enterprise governance, risk, and regulatory expectations

• Define, set and maintain appropriate controls directly embedding in the solution and/or leveraging on AI SPM solutions

1. Hands-on Technical Leadership

• Act as a credible engineering leader, able to guide architecture, software design, platform choices, delivery quality, and production readiness.

• Bring strong experience in modern application development, APIs, cloud-native engineering, data platforms, DevSecOps, automation, and AI/ML engineering.

• Ensure AI solutions are not only innovative but also secure, scalable, maintainable, observable, and integrated into enterprise technology standards.

• Challenge technology decisions and improve engineering maturity across the team.

• Bring working knowledge of:

  • AI/ML/GenAI architectures (including Agentic, MCP, AI gateways, RAG, LLM, CSP AI services, AI framework such as langchain / langsmith, giskar, gandalf, security guardrails)
  • data pipelines and model lifecycle
  • AI risks and mitigation strategies

1. Governance & Operating Model

• Embed AI into enterprise risk and governance frameworks

• Define structured processes for:

  • AI use-case assessment
  • prioritization and validation
  • risk visibility and reporting

• Support leadership on regulatory readiness, controls, and audit expectations

1. Build & Lead a High-Impact Team

• Build and scale a high-performing team of AI and cybersecurity experts

• Define capabilities across:

  • AI security, AI engineering, cyber use-case delivery, and risk governance

• Foster collaboration across cybersecurity, data, and AI teams

• Drive AI capability development across the organisation

Profile You are a seasoned technology and software engineering leader with strong hands-on experience in AI/GenAI delivery and a solid understanding of cybersecurity. You have led engineering teams, built production-grade platforms or applications, and can translate complex cyber problems into scalable technology solutions.

You do not need to be a career cybersecurity specialist, but you must be able to understand cyber use cases, engage credibly with security stakeholders, and ensure that solutions are secure by design.

practitioner + cyber-aware solution builder.