Senior Lead Engineer - Agentic AI

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Date: 13 Jul 2026

Location: SG

Company: Synapxe

Position Overview

This is an AI engineering role applied to cybersecurity. The role will define and build the agentic AI harness, control plane, model evaluation framework, AI-to-system interface layer, memory and knowledge architecture, guardrails, observability model and production standards needed to deploy AI agents safely across cyber functions.

The role will support agentic AI capabilities across cybersecurity, including security operations, incident response, threat intelligence, detection engineering, vulnerability management, application security, cloud security, identity and access management, GRC, control testing, red teaming, purple teaming, security engineering, email security, data security and executive cyber reporting.

Role & Responsibilities

  • Design secure agentic AI architectures supporting planning, reasoning, tool use, memory, retrieval, model routing, multi-agent coordination and human-in-the-loop workflows.
  • Build the agent harness and control plane to manage autonomy, policies, approvals, audit logs, rollback, kill switches and agent action boundaries.
  • Implement AI-to-cyber integrations with SIEM, SOAR, EDR, IAM, PAM, CMDB, ITSM, scanners, cloud platforms, repositories, CI/CD, ticketing and knowledge systems.
  • Build secure tool mediation to define what agents can read, recommend, draft, test, execute or escalate, with approvals for high-risk actions.
  • Define agent identity and access controls using least privilege, scoped credentials, JIT access, secrets isolation, session boundaries and full auditability.
  • Secure the agentic AI supply chain across prompts, tools, connectors, MCP servers, plugins, packages, containers, models, datasets and retrieval sources.
  • Build the cyber data, memory and knowledge layer using RAG, vector search, knowledge graphs, case memory and context stores.
  • Ensure agent outputs are evidence-based, traceable to source systems, alerts, logs, tickets, vulnerabilities, threat intelligence and case notes.
  • Develop reusable cyber agent patterns for triage, investigation, threat intelligence, vulnerability analysis, secure code review, detection, GRC and remediation.
  • Evaluate frontier and open-source models for reasoning, coding, tool use, cyber performance, reliability, hallucination, latency, cost, safety and deployment fit.
  • Design model-agnostic architecture to support model routing, fallback, regression testing, cost controls, latency targets and graceful degradation.
  • Build AI evaluation and test harnesses covering benchmarks, adversarial tests, simulations, incident replay, human review and operational acceptance criteria.
  • Create cyber simulation environments to safely test agents against historical incidents, SOC cases, vulnerable code, phishing, cloud attack paths and red-team scenarios.
  • Design controls against prompt injection, malicious documents, poisoned tickets, hostile webpages, compromised retrieval sources and memory poisoning.
  • Build authorised AI-assisted cyber assessment capabilities for code review, vulnerability discovery, exploit validation, patch suggestions, testing and red-team planning.
  • Define human decision rights, ownership, approvals, monitoring responsibilities and clear boundaries where AI can assist versus where humans must decide.
  • Design for production operations, including monitoring, logging, rollback, runbooks, ownership, access reviews, cost controls, LLMOps and lifecycle management.
  • Partner with cyber SMEs to convert operational workflows into safe, measurable, production-grade AI capabilities with clear controls and escalation paths.

Requirements

  • Strong hands-on experience building production-grade LLM, agentic AI, ML, automation or platform systems.
  • Deep understanding of agent architecture, orchestration frameworks, tool calling, memory design, RAG, model routing and multi-agent workflows.
  • Experience with frontier models, open-source models or both, including evaluation, benchmarking and model comparison.
  • Strong software engineering background, including Python, APIs, backend services, cloud platforms, containers, CI/CD, authentication, logging and production observability.
  • Experience integrating AI systems with enterprise APIs, identity systems, data platforms, workflow engines, ticketing systems, code repositories and operational tools.
  • Prior experience operating or supporting production systems, including monitoring, alerting, incident response, rollback, release management, access control, cost management and post-incident review.
  • Practical understanding of production failure modes such as model drift, prompt regressions, broken tool calls, API failures, retrieval errors, permission issues, latency problems, data quality gaps, cost spikes and unsafe outputs.
  • Practical understanding of AI safety risks, including hallucination, prompt injection, insecure tool use, excessive agency, sensitive data leakage, memory poisoning, adversarial manipulation and unsafe autonomous behaviour.
  • Experience designing human-in-the-loop workflows for high-risk, regulated or security-sensitive environments.
  • Ability to design for operational handover, including runbooks, support models, service ownership, observability, change control and measurable service health.

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