AI Engineer with PAM

Job Title: PAM Engineer AI

Location: New York, NY (Hybrid 3 days)

Duration: 11 months

Job Summary:

Key Responsibilities:

• 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.

Required Skills:

• 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.

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 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.