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.

• Lead hands?on development of AI?enabled and LLM?based applications, including agentic and automation?driven systems.
• Design and implement agent orchestration architectures, including task decomposition, multi?agent coordination, tool/function invocation, state and memory management, and policy?aware execution flows.
• Engineer robust LLM interaction layers, including prompt design, grounding strategies (e.g., RAG), tool integration, feedback loops, and evaluation mechanisms.
• Own end?to?end AI system architecture, spanning APIs, services, data pipelines, model serving, and observability.
• Ensure AI solutions operate effectively across cloud?native and hybrid environments, with attention to scalability, latency, and reliability.
• Embed security, compliance, and governance?by?design, including access controls, logging, traceability, explainability, and human?in?the?loop safeguards.
• Provide technical leadership through architecture ownership, hands?on coding, design reviews, and mentorship of AI engineers.

• Proven experience as a hands?on 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 multi?agent systems, tool?using agents, and agent lifecycle management.
• Solid background in cloud?native 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 production?ready 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.

• 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 non?human identities, including least?privilege and JIT patterns.

Familiarity of frameworks such as OWASP Top 10 for Agentic and LLM Applications, MITRE ATLS and NIST AI RMF