PAM engineer

primarily a traditional PAM (Privileged Access Management) Engineer role with AI experience being a secondary requirement. The core focus is on strong PAM expertise and candidates should be evaluated first and foremost on their hands-on experience with PAM technologies and related security practices.

Candidates should also have a solid understanding of AI and some practical experience applying or leveraging AI within a PAM or cybersecurity environment. However PAM expertise remains the most critical requirement for this role.

Key Responsibilities:

• Lead handson development of AIenabled and LLMbased applications including agentic and automationdriven systems.
• Design and implement agent orchestration architectures including task decomposition multiagent coordination tool/function invocation state and memory management and policyaware execution flows.
• Engineer robust LLM interaction layers including prompt design grounding strategies (e.g. RAG) tool integration feedback loops and evaluation mechanisms.
• Own endtoend AI system architecture spanning APIs services data pipelines model serving and observability.
• Ensure AI solutions operate effectively across cloudnative and hybrid environments with attention to scalability latency and reliability.
• Embed security compliance and governancebydesign including access controls logging traceability explainability and humanintheloop safeguards.
• Provide technical leadership through architecture ownership handson coding design reviews and mentorship of AI engineers.

Required Skills:

• Proven experience as a handson AI or platform engineer with leadership responsibility for production systems.
• Deep expertise in LLMs including model selection prompt engineering grounding techniques evaluation and mitigation of hallucination and drift.
• Strong experience with agent frameworks and orchestration patterns including multiagent systems toolusing agents and agent lifecycle management.
• Solid background in cloudnative architecture APIs distributed systems and modern MLOps/LLMOps practices.
• Ability to translate business risk and regulatory requirements into concrete technical designs and implementations.
• Experience translating advanced AI (LLMs agentic workflows orchestration) into secure governed and auditable capabilities that are productionready for large regulated enterprises.
• Experience with real-world deployments scenarios ensuring AI solutions work reliably across hybrid environments integrate with enterprise platforms and deliver measurable business outcomes.

Preferred Qualifications:

• Experience implementing AI control frameworks (e.g. model controls guardrails evaluation and auditability) aligned to NIST ISO or sector regulators.
• Knowledge of identity access and authorization models for agents and nonhuman identities including leastprivilege and JIT patterns.