Distinguished AI Engineer

Distinguished AI Engineer


Enterprise AI Architecture / LLM Platforms / Advanced AI Systems


Level


Distinguished / Principal Technical Leader


Target / alternate titles


Distinguished Engineer - AI; Principal AI Engineer; AI Platform Architect; Chief AI Architect; Staff ML Engineer; Principal ML Platform Engineer


Core keywords


enterprise AI architecture, AIRP, LLM platform, AI gateway, model evaluation, observability, RAG, agentic AI, governance, AWS, cloud-agnostic architecture, Terraform modules, IaC, DevOps pipelines, Kubernetes, GPU


Recruiter red flags


Pure research leader with limited production architecture; narrow single-product ownership; no AWS/cloud architecture depth; no Terraform/IaC standards; no governance, security, or operating-model experience.

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 AIRP is scalable, reusable, observable, cost-efficient, secure, and compliant, while remaining AWS-first today and cloud-agnostic by design.


Client-specific emphasis

• Own the cloud-agnostic AI blueprint while leveraging AWS as the current implementation platform.
• Define reusable Terraform/IaC and DevOps pipeline patterns that can be federated across multiple AIRP use cases.
• Ensure architecture supports AI for business, AI for engineering, and responsible citizen development without fragmenting controls.

Primary ownership

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

Key responsibilities

• Define target-state architecture for AIRP, 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.
• Create AWS implementation patterns that align with a cloud-agnostic blueprint and minimize unnecessary vendor lock-in.
• Define standards for Terraform modules, reusable IaC templates, environment strategy, pipeline promotion, approvals, observability, secrets, rollback, and operational controls.
• 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, risk, security, and governance forums.
• Partner with cybersecurity, risk, compliance, legal, audit, product, business, and citizen-development enablement teams.
• Assess emerging AI technologies and recommend adoption based on business value, maturity, risk, cost, portability, 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 AWS cloud architecture experience or comparable hyperscaler depth, with clear ability to design cloud-agnostic AI platform patterns.
• Experience shaping Terraform/IaC standards, DevOps pipelines, secure deployment patterns, observability, resiliency, and platform operating models.
• Ability to influence senior stakeholders and operate across business, technology, risk, compliance, security, data, 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, multi-cloud AI strategy, or platform blueprint ownership.
• Familiarity with Responsible AI, model risk management, audit expectations, technology risk controls, and citizen development governance.

Initial screening questions

• What enterprise AI platform or architecture did you define, and how broadly was it adopted?
• How would you design an AWS-hosted AI platform that must remain cloud-agnostic over time?
• How would you standardize Terraform modules, CI/CD pipelines, model evaluation, monitoring, and release management across teams?
• Describe a major technical trade-off you made between cost, latency, risk, portability, and capability.
• How do you design a secure LLM platform for regulated enterprise use?
• How do you influence teams that do not directly report to you?