Posted 16 August, 2026

Head of AI Engineering & Cyber Solutions

Societe Generale

India-Bangalore Full Time

Reference: 396_132173_26000CLB

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

2. 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.

3. 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

4. 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

5. 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

6. 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

7. 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.

Leadership & Soft Skills

Strong executive presence and ability to influence senior stakeholders
Able to combine strategy, execution, and technical depth
Comfortable working in global, matrixed environments

Certifications (Preferred)

CISSP, CISM, CISA, GIAC (e.g., GML, GCSA), or equivalent
AI/ML security certifications or equivalent demonstrated expertise

What Makes This Role Unique

Opportunity to build a new AI engineering capability for cybersecurity from India.

Combines software engineering leadership, AI innovation, and cybersecurity domain impact.

High-visibility role with potential to progressively own broader IT development for the function.

Requires a rare profile: technology delivery leader hands-on AI practitioner cyber-aware solution builder.

Experience

15 years of progressive experience in software engineering, IT development, platform engineering, or technology delivery, with significant leadership responsibility.

Proven experience leading engineering teams delivering enterprise-grade applications, platforms, automation, data, or AI-based solutions.

Strong hands-on experience in AI/ML/GenAI solution delivery, including LLM-based applications, RAG, agentic workflows, AI platforms, or applied AI use cases.

Solid understanding of cybersecurity domains such as security operations, vulnerability management, application security, cloud security, threat detection, or cyber risk.

Experience delivering solutions in financial services, cybersecurity, risk, or other regulated environments is strongly preferred.

Prior experience in Financial Services, Banking, or a regulated industry is strongly preferred

Technical Expertise

Strong knowledge of modern programming and engineering ecosystems, preferably Python and/or Java, APIs, cloud services, container platforms, data engineering, and automation.

Experience designing scalable, reliable, secure, and maintainable enterprise applications.

Hands-on understanding of AI engineering patterns such as RAG, vector databases, prompt engineering, orchestration frameworks, agentic AI, model evaluation, and AI guardrails.

Deep understanding of AI/ML architectures, model development lifecycles (MLOps), and the associated threat landscape
Strong command of AI security domains: adversarial robustness, model governance, data security for AI, GenAI risk (prompt injection, jailbreaking, hallucination risk), and agentic AI security
Broad and current knowledge of Microsoft AI solutions and ecosystem (Azure OpenAI, Azure AI Foundry, Copilot/ Powerplatform etc.) or AWS AI components (Bedrock, AI services ..).
Solid grounding in cloud security (Azure, AWS, GCP) as it applies to AI workloads
Proficiency in modern data platforms (Databricks, Snowflake) and their security considerations
Working knowledge of Python or similar scripting/programming experience (advantageous: langchain, langraph)