AI Engineer - Cybersecurity Products at ASSURITY GLOBAL PTE LTD

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

19 Apr, 26

Salary

0.0

Posted On

19 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, Cybersecurity, LLMs, Data Pipelines, Classical ML, Evaluation, Safety, Guardrails, Retrieval-Augmented Generation, Cloud, MLOps, Python, SQL, NoSQL, Containerization, CI/CD

Industry

IT Services and IT Consulting

Description

Assurity Trusted Solutions (ATS) is a wholly owned subsidiary of the Government Technology Agency (GovTech). As a Trusted Partner over the last decade. ATS offers a comprehensive suite of products and services ranging from infrastructure and operational services, governance and assurance services as well as managed processes. In a dynamic digital & cyber landscape where trust & collaboration is key, ATS continues to drive mutually beneficial business outcomes through collaboration with GovTech, government agencies and commercial partners to mitigate cyber risks and bolster security postures. We are hiring an AI Engineer to build agentic AI systems for cybersecurity use cases. This role blends LLMs with solid AI/ML fundamentals – data pipelines, classical ML where it fits, rigorous evaluation, and safety/guardrails – to ship reliable, auditable services. This will be on a direct contract with us till 31 March 2027, subjected to extension based on performance. Responsibilities: Design, build, and ship agentic AI features (planning/execution loops, tool use/function calling, multi-step workflows) for security use cases such as vulnerability triage, exploit reproduction assistance, and incident-response copilots. Implement and harden retrieval-augmented generation (RAG): indexing, chunking, routing, re-ranking, feedback loops, and data governance for sensitive environments. Set up evaluation & observability for LLM/agent workflows (tracing, cost/latency/quality dashboards, offline+online evals, guardrail hit rates) and turn insights into product changes. Build safety & guardrails (content policies, schema/output validation, PII redaction, prompt-injection/jailbreak defenses, tool permissioning) and monitor them in production. Apply traditional ML (classification, regression, anomaly detection) where it’s simpler or more effective than LLMs; run A/B tests and error analysis to choose the right approach. Own productionization: CI/CD for AI apps, containerization, scalable inference endpoints, vector/search infra, runbooks, and SLOs for reliability. Collaborate with product and security teams to scope problems, write concise design docs, and iterate quickly while meeting security and privacy requirements. Perform other duties as assigned; responsibilities may evolve with product needs. Technical Requirements LLM/Agent systems: built agents or multi-tool chains (function calling, tool routing, planning/feedback loops) using frameworks like LangChain/AutoGen/CrewAI or custom orchestration. RAG stack: knowledge of embedding models, vector stores, hybrid search, re-rankers, freshness and authorization filters, prompt templating, and caching. Evaluation & observability: design eval harnesses (golden sets, rubric/LLM judges), tracing (e.g., OpenTelemetry/Langfuse), and dashboards for quality, cost, and latency; run A/B tests. Safety & guardrails: knowledge of policy enforcement, output validation (schemas/JSON), least-privilege tool access, prompt-injection/jailbreak mitigations, secrets handling, and PII redaction. ML fundamentals: hands-on with sklearn/XGBoost/LightGBM; data splitting, cross-validation, calibration, and metrics (precision/recall, ROC/PR). Cloud & MLOps: experience on AWS/Azure/GCP; Docker/Kubernetes; CI/CD; IaC (Terraform/CloudFormation); deploying and monitoring ML/LLM services. Software engineering: strong Python proficiency; testing (unit/integration), code reviews, version control, API design, and readable design docs. Data & storage: familiarity with vector databases plus working knowledge of SQL/NoSQL. Qualifications Experience: 2 or more years in AI/ML/LLM engineering or software engineering with meaningful AI/ML contributions (exceptional internships/research/startup projects welcome). Proven delivery of at least one production AI feature, ideally an LLM/agent workflow with RAG, evaluation, and monitoring. Clear communicator who can scope problems, write concise design proposals, and partner with PMs/security stakeholders. Working knowledge of cybersecurity concepts (e.g., OWASP Top 10, OAuth/OIDC, data protection) and motivation to solve security problems with AI. If you are new to cybersecurity, that’s okay — we welcome candidates who are eager to learn and grow in this domain! Join us and discover a meaningful and exciting career with Assurity Trusted Solutions! The remuneration package will commensurate with your qualifications and experience. Interested applicants, please click "Apply Now". We thank you for your interest and please note that only shortlisted candidates will be notified. By submitting your application, you agree that your personal data may be collected, used and disclosed by Assurity Trusted Solutions Pte. Ltd. (ATS), GovTech and their service providers and agents in accordance with ATS’s privacy statement which can be found at: https://www.assurity.sg/privacy.html or such other successor site. A wholly-owned subsidiary of GovTech. An attractive yearly training budget and annual performance bonus! Contract Staff enjoys the same benefits as Permanent Employees.

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Responsibilities

The AI Engineer will design and build agentic AI features for cybersecurity use cases, focusing on areas like vulnerability triage and incident response. They will also implement safety measures and monitor AI systems in production.