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Agentic AI Engineer LLM, Python

CÔNG TY TNHH NEXLAB IT SOLUTIONS · Hồ Chí Minh
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Toàn thời gian
Hình thức
Tại văn phòng
Cấp bậc
Nhân viên
Ngành nghề
Kỹ thuật / Cơ khí
Mức lương
Thương lượng
Địa điểm
Not Available, Hồ Chí Minh, Hồ Chí Minh

Tổng quan

  • Offensive Platform — An autonomous security platform that uses agentic AI to continuously penetration-test APIs, web applications, and cloud infrastructure. It validates vulnerabilities, chains attack paths, and generates audit-ready reports — all with human-in-the-loop safety
  • Defensive Platform — A role-based AI SOC platform where specialized agents (SOC Manager, Analyst, Incident Response, Threat Hunter) work as a coordinated crew to triage alerts, investigate incidents, hunt threats, and manage Detection-as-Code — at machine speed, under human oversight.
  • Design and implement autonomous agent architectures that handle multi-step reasoning, tool use, memory, and planning in production security workflows
  • Build reliable agent loops with proper error handling, retry logic, guardrails, and human-inthe-loop approval gates for high-risk actions
  • Develop dynamic tool-calling pipelines where agents select, configure, and orchestrate external security tools based on contextual analysis
  • Engineer prompt chains and agent reasoning strategies across multiple LLM providers (OpenAI, Google Vertex AI, Anthropic Claude) with model-agnostic abstractions
  • Build and maintain the context layer that gives agents awareness of users, assets, past incidents, typical behavior, and environmental state
  • Design evaluation frameworks to measure agent reliability, accuracy, and safety — especially for high-stakes actions like exploit execution or incident response recommendations
  • Collaborate with security engineers to translate offensive and defensive domain expertise into agent behavior, tool profiles, and decision logic
  • Optimize for latency, cost, and token efficiency in production agent workloads
  • Support on-premise deployments using self-hosted open-source models (DeepSeek, Llama) for air-gapped enterprise customers
  • Your Skills and Experience
  • Must Have
  • 4+ years of software engineering experience with strong proficiency in Python
  • 1+ year of hands-on experience building LLM-powered agent systems — not chatbots, but agents that reason, plan, use tools, and take multi-step actions
  • Deep working knowledge of at least one agentic framework: LangGraph, LangChain, CrewAI, AutoGen, or equivalent
  • Experience with tool-calling / function-calling patterns, including dynamic tool selection and chaining
  • Solid understanding of prompt engineering for complex reasoning tasks — chain-of-thought, ReAct, plan-and-execute patterns
  • Experience integrating with multiple LLM providers (OpenAI, Anthropic, Google) and managing model-agnostic abstractions
  • Ability to build robust, production-grade systems — not just prototypes. You think about failure modes, retries, timeouts, guardrails, and observability
  • Strong fundamentals in distributed systems, async programming, and API design
  • Strong Plus
  • Background in cybersecurity — offensive (pentesting, vulnerability assessment, red teaming) or defensive (SOC operations, SIEM, detection engineering, incident response)
  • Familiarity with MITRE ATT&CK framework, Sigma rules, or detection-as-code practices
  • Experience deploying and fine-tuning open-source LLMs (Llama, DeepSeek, Mistral) for onpremise or air-gapped environments
  • Knowledge of graph databases (Neo4j) for modeling attack paths or entity relationships
  • Experience building multi-agent systems where multiple specialized agents collaborate, delegate, and share context
  • Familiarity with RAG pipelines, vector databases, and knowledge retrieval systems
  • Mindset
  • You're excited about building AI systems that take real action in the world, not just generate text
  • You have a strong security intuition — you think about what can go wrong, what an adversary would do, and where guardrails are needed
  • You're comfortable with ambiguity and can translate vague domain requirements into concrete agent behaviors
  • You care about reliability over cleverness — a working agent that handles edge cases beats a flashy demo
  • Why You'll Love Working Here
  • Opportunity to build AI agent systems for two products simultaneously — offensive and defensive security — a rare engineering challenge
  • Direct influence on product architecture and AI strategy from day one
  • Work with a team that understands both security and AI deeply

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