JOB SUMMARY
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.
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.
Required Qualifications
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 Qualifications
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.
