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1–2 jobs· newest first

Cyber Security Engineer - Sr. Consultant level (AI Engineer)
Visa · Singapore
On-site · Senior
Senior AI Developer
ClickUp · Remote, US
Remote · Full-time · Senior

+10K new in the last hour

Cyber Security Engineer - Sr. Consultant level (AI Engineer)

Visa · Posted Sep 18, 2026

Singapore
On-site
Senior
8+ years

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AI

Apply on careers page

Senior AI Engineer to build next-generation cybersecurity AI products for enterprise security teams

Responsibilities

Build Cybersecurity AI Products
Apply Strong AI Engineering Practices
Develop Production-Grade Software
Continuously Learn, Research and Evaluate Emerging AI technologies

Requirements

Bachelor’s Degree
Advanced Degree (Master’s, MBA, JD, MD)
PhD
Nice to have: Graduate degree in computer science or related fields
Nice to have: Experience building AI agents

Skills

LLM applications Agentic AI systems Retrieval-augmented generation Tool orchestration Context management Model evaluation Cloud-native software engineering APIs Secure software development Cybersecurity concepts Security operations Cloud security Threat intelligence SIEM/SOAR workflows Vulnerability management Incident response Kubernetes Infrastructure as code Observability platforms Secrets management Cloud-native security MITRE ATT&CK OWASP NIST Cybersecurity Framework CIS Controls ISO 27001 LLM evaluation methods Golden datasets Human review workflows Regression testing Vector search Embeddings Prompt engineering System prompts Instruction design Vector databases Knowledge graphs Model benchmarking Feedback loops Agent memory Evaluation datasets OpenAI Assistants APIs LangChain LlamaIndex Semantic Kernel AutoGen CrewAI OpenAI Assistants

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Senior AI Developer

ClickUp · Posted Nov 6, 2026

Remote, United States
Remote
Full-time · Senior
5+ years

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Senior AI Engineer responsible for building and evolving AI evaluation systems, core agent harness, and scalable AI platform services at ClickUp.

Responsibilities

Design, build, and own ClickUp's AI evaluation system, including golden datasets, LLM-as-judge and automated scorers, regression detection, and quality tracking across AI features.
Build and evolve the core agent harness, and the AI that performs well on those evals, partnering closely with product and engineering to define what "great" looks like.
Build self‑improvement loops where agents modify other agents or prompts, rerun evals, and iterate automatically.
Benchmark new models as they're released and own model selection, routing, and fallback strategies across providers (e.g., OpenAI, Anthropic, Google, open-weight).
Architect, design, and implement scalable AI platform services that support the deployment, orchestration, and lifecycle management of LLMs and other AI models.

Requirements

Nice to have: Background in data science
Nice to have: Applied science
Nice to have: Machine learning engineering
Nice to have: Eval tooling
Nice to have: Braintrust

Skills

distributed systems

cloud‑native technologies

statistical rigor

model benchmarking

cost/latency tradeoffs

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