Distinguished AI Engineer

Title: Distinguished AI Engineer

Location: Jersey City NJ(Hybrid)

Type: Contract

Role purpose:

Set the architecture engineering standards platform strategy and technical governance for enterprise AI and GenAI capabilities. This role guides senior engineering teams and ensures AI platforms are scalable reusable observable cost-efficient and compliant with enterprise risk expectations.

Primary ownership:

• Target-state enterprise AI architecture reference patterns and platform guardrails.
• LLMOps AI gateways model-serving strategy evaluation platforms observability governance and operating standards.
• Technical assurance for high-risk or high-impact AI initiatives.

Key responsibilities:

• Define target-state architecture for LLM platforms model hubs AI gateways RAG services agent frameworks orchestration layers and model-serving infrastructure.
• Establish enterprise standards for AI SDLC LLMOps MLOps evaluation release management operational resilience and production support.
• Guide architecture for secure scalable and cost-efficient inference across cloud hybrid private and containerized environments.
• Define guardrail patterns for hallucination mitigation bias monitoring harmful-content controls prompt injection defense data leakage prevention and human oversight.
• Lead design reviews for critical AI systems and provide technical assurance to architecture and risk forums.
• Partner with cybersecurity risk compliance legal audit product and business teams to align AI designs with enterprise controls.
• Mentor principal engineers and establish reusable design patterns reference implementations and engineering playbooks.
• Assess emerging AI technologies and recommend adoption based on business value maturity risk cost and regulatory fit.

Must-have candidate profile:

• 10 years in AI/ML systems distributed systems enterprise architecture or platform engineering.
• Deep experience with LLMs RAG embeddings model serving AI orchestration evaluation frameworks and AI infrastructure.
• Proven track record defining architecture and technical standards across multiple engineering teams.
• Strong understanding of distributed training GPU infrastructure inference optimization observability model governance and resiliency.
• Ability to influence senior stakeholders and operate across business technology risk compliance and architecture forums.

Preferred experience:

• Global bank fintech financial-services or regulated-enterprise platform experience.
• Experience with enterprise AI platforms private LLM deployments internal model hubs AI gateways or multi-cloud AI strategy.
• Familiarity with Responsible AI model risk management audit expectations and technology risk controls.
• Initial screening questions
• What enterprise AI platform or architecture did you define and how broadly was it adopted
• How do you design a secure LLM platform for regulated enterprise use
• How would you standardize model evaluation monitoring and release management across teams
• Describe a major technical trade-off you made between cost latency risk and capability.
• How do you influence teams that do not directly report to you