Cybersecurity and AI Software Engineer

Assurity Trusted Solutions Pte Ltd

Singapore

On-site

SGD 70,000 - 90,000

Full time

14 days+

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Benefits offered by this job

Annual performance bonus

Training budget

Benefits comparable to Permanent Employees

Job summary

A cybersecurity-focused organization in Singapore seeks an experienced AI Engineer to design and implement AI features for security applications. The candidate should have over 2 years in AI/ML engineering, strong Python skills, and a willingness to learn about cybersecurity. This role offers a competitive remuneration package and various benefits, including training budgets and performance bonuses.

Qualifications

Experience in designing AI features for security use cases.
Understanding of cybersecurity concepts.
Ability to collaborate and communicate effectively.

Responsibilities

Design and build AI features for security.
Implement and harden retrieval-augmented generation.
Collaborate with product and security teams.

Skills

Agentic AI features design

Retrieval-augmented generation knowledge

Safety and guardrails

Traditional ML techniques

Productionization of AI apps

Collaboration with product teams

Cloud services experience

Python proficiency

Data familiarity

Education

2+ years in AI/ML/LLM engineering

Tools

LangChain

AWS

Docker

SQL

Job description

Overview

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 and cyber landscape where trust and collaboration are key, ATS drives mutually beneficial business outcomes through collaboration with GovTech, government agencies and commercial partners to mitigate cyber risks and bolster security postures.

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 and observability for LLM/agent workflows (tracing, cost/latency/quality dashboards, offline and online evals, guardrail hit rates) and turn insights into product changes.
Build safety and 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 infrastructure, 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 and observability: design eval harnesses (golden sets, rubric/LLM judges), tracing, and dashboards for quality, cost, and latency; run A/B tests.
Safety and 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 and 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 and 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 and 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.

Benefits