AI Engineer
# AI Engineer **U3 PROJECTS PTE. LTD.** · Singapore · `On-site` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Indeed* --- ### About the Role S ummary: - The successful candidate is expected to build and operationalize AI, automation and data solutions that improve cybersecurity analytics, decision-making and operational response. - The person will work with cybersecurity specialists and technology teams to turn business needs into secure, reliable and scalable solutions. Key Responsibilities: - Develop AI-enabled applications, agents, workflows and automation for cybersecurity use cases. - Translate operational challenges into testable use cases, prototypes and production solutions. - Integrate AI models with enterprise systems, APIs, data pipelines and cloud services. - Establish testing, evaluation, documentation, monitoring and operational handover practices. - Apply appropriate security, access control, human oversight and failure-handling safeguards. Skills: - Strong software development experience, particularly in Python and modern application frameworks. - Hands-on experience in building Large Language Model (LLM) applications, agents, retrieval solutions or multi-step orchestration workflows. - Experience with APIs, data integration, cloud development (preferably AWS). - Working knowledge of DevOps or CI/CD practices, including automated testing, version control, secrets management, containerization, deployment pipelines, and production diagnostics. - Strong analytical, problem-solving and stakeholder collaboration skills. - Possess positive and collaborative mindset. - Good written and verbal communication skills. - Agile, fast learner and able to adapt to changes. Good to have: Experience in one or more of the following areas is advantageous. Candidates are not expected to cover all three groups. 1. Cybersecurity Security operations (SecOps), such as vulnerability management, threat intelligence, incident response or detection engineering. Red and blue teaming, including testing AI systems for misuse and failure modes. Enterprise identity, network, proxy, governance or data access controls. 2. Data and platforms Data engineering or data lake house platforms, such as Databricks. Open Cybersecurity Schema Framework (OCSF), security data normalization or cross-source event correlation. Full-stack product development and UI/UX. 3. AI evaluation and serving LLM evaluation, benchmarking, model selection or finetuning. Model serving, inference optimization, GPU infrastructure or local model deployment.
Наблюдалась 2026-10-06, впервые 2026-10-06, источник — Indeed.