Senior Executive, AI Engineer

Job Purpose
In an increasingly complex cyber threat landscape, the Senior Executive, AI Engineer is responsible for leading the technical integration of Artificial Intelligence (AI) and Large Language Models (LLMs) into SMRT’s cyber defence operations. The role focuses on architecting and deploying agentic workflows to automate Tier-1 incident triage and developing generative AI capabilities to accelerate hypothesis-driven threat hunting. This includes fine-tuning models on internal telemetry and ensuring the governance, security, and efficacy of AI-driven security tools.

Responsibilites
As Senior Executive, AI Engineer, you will support the design, implementation and operationalisation of SMRT’s threat management programme. Your responsibilities include:

• Operationalise AI-driven threat management by collaborating with SOC, threat intel, incident response, vulnerability management and other cybersecurity teams.
• Engineer and deploy autonomous AI agents capable of performing pre-triage on security alerts.
• Develop LLM-driven assistants (e.g., Text-to-SQL/SPL converters) that enable Threat Analysts to rapidly generate hunt queries and correlate disparate data points for stealthy intrusion detection.
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines that ingest internal documentation (playbooks, past incident reports) and external threat intelligence to provide context-aware answers to analysts.
• Fine-tune open-source or proprietary models on SMRT’s specific network telemetry and log data to improve anomaly detection accuracy and reduce hallucination rates in security outputs.
• Work closely with the SOC Manager to define triage logic for agents and the Threat Management Manager to define hunt hypotheses for AI automation.
• Continuously monitor the performance of AI agents and models, establishing feedback loops with human analysts to retrain models and improve decision-making accuracy over time.

Qualifications & Work Experience

• A bachelor’s degree in Cybersecurity, Information Systems, Computer Science, or a related discipline.
• 3-8 years of relevant experience in threat management, threat hunting, incident response, red teaming, cybersecurity operations, or equivalent intelligence driven security roles.
• Hands-on experience in developing, fine‑tuning, and operationalising AI/LLM models for cybersecurity use cases (e.g., threat detection, enrichment automation, anomaly identification, and intelligence summarisation).
• Strong understanding of threat actor behaviours, tactics, techniques and procedures (TTPs), and threat modelling frameworks (e.g., MITRE ATT&CK, Diamond Model, Cyber Kill Chain).
• Hands‑on experience working with threat management (e.g. External Attack Surface Monitoring (EASM)) platforms, threat feeds, OSINT tools, TIPs, malware analysis tools, and enrichment technologies will be advantageous.
• Familiarity with security operations, incident response, and vulnerability management workflows, enabling effective intelligence operationalisation.
• Knowledge of regulatory requirements including the Cybersecurity Code of Practice (CCoP), Personal Data Protection Act (PDPA), and sector-specific standards are advantageous.
• Industry certifications are advantageous (e.g., GCTI, GREM, GCIA, CISSP, CISM, CEH, FOR508/Threat Hunting, SANS intelligence‑related certifications).

Technical Skills include:

• Proficiency in developing, fine‑tuning, and operationalising AI/LLM models.
• Strong understanding of cyber threat management methodologies.
• Deep knowledge of adversary tactics, techniques and procedures (TTPs), threat actor profiling, campaign analysis, and threat modelling frameworks (e.g., MITRE ATT&CK, Diamond Model, Cyber Kill Chain).
• Expertise in designing autonomous agent workflows that can execute multi-step security tasks
• Strong scripting skills to integrate AI models with SOAR APIs, SIEM backends, and EASM platforms for real-time data exchange.

Core Competencies include:

• Ability to conceptualise novel AI use-cases and lead the technical execution from proof-of-concept to full operationalisation.
• Effective communicator with the ability to engage stakeholders across technical and non-technical domains.
• Ability to translate complex cybersecurity requirements into technical data science specifications (and vice versa) for stakeholders
• High integrity and discretion in handling sensitive information.
• A strong commitment to responsible AI use, ensuring transparency, explainability, and human-in-the-loop oversight for all automated decisions.
• Proactive and collaborative mindset, with a commitment to continuous improvement.