Principal Engineer – AI

🔥 0 minutes ago

🏄 California, Florida, +3 more states – Remote

🏄 California – Remote 🐊 Florida – Remote 🌽 Illinois – Remote 🗽 New York – Remote 🤠 Texas – Remote

💵 $112.2k - $209k / year

⏰ Full Time

🔴 Lead

🤖 AI Engineer

👻 Ghost score 0%

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BMO U.S.

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🛡️ Insurance

💼 Consulting

📦 Logistics

Insurance • Consulting • Logistics

BMO U. S. is a diversified financial services company operating in the United States. It offers a broad range of financial products and services including personal and business banking, mortgage services, investments, financial planning, insurance, and wealth management. Additionally, it provides commercial loans, commercial mortgages, and other financial solutions tailored for small businesses and large enterprises. The company places a strong emphasis on customer service and offers digital and cross-border banking solutions to meet the needs of diverse clients. BMO U. S. is also involved in asset management and capital markets operations, making it a full-service financial institution.

📋 Description

• Build and operate core Enterprise AI Platform infrastructure, including the AI Gateway, Policy Engine, Identity Fabric, AI Registry, Guardrails Runtime, and AI Observability • Own platform capabilities end to end: design, implement, test, ship, and operate production infrastructure • Build, configure, harden, scale, enhance, and govern enterprise-grade platform capabilities • Develop a federated AI Registry with self-service onboarding, lifecycle workflows, and external registry federation • Engineer policy-as-code infrastructure, policy compilation and distribution, approval workflows, and policy simulation • Build telemetry pipelines, trace correlation, lineage-stamped traces, and a tamper-evident audit lake producing regulator-ready evidence • Implement certification workflows, compliance scoring, decommission governance, and evidence generation • Develop gateway runtime capabilities across AWS and Azure, including policy evaluation, routing, residency, budget/quota controls, circuit breaking, and cross-region failover • Build multi-stage guardrails for moderation, prompt-injection defense, PII, output validation, hallucination detection, policy enforcement, and agentic workload protections • Implement AI workload identity, token exchange, trust boundaries, Entra Agent ID integration, identity propagation, and cross-cloud token federation • Engineer operability and defensibility through instrumentation, SLOs, latency budgets, failure-mode planning, and runtime evidence • Build APIs, interfaces, and integrations for domains, DevOps pipelines, and enterprise systems • Assess emerging AI infrastructure, foundation-model access patterns, and standards to make cost-aware engineering choices • Set engineering standards, review designs and code, mentor team members, and grow technical depth • Partner with AI Developer Experience, AI Security, AI SDLC, and the Senior AI Architect • Participate in on-call operations for owned services

🎯 Requirements

• Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline (Master's preferred) • 8+ years of software/platform engineering experience for Principal level, or 5+ years for Senior level • Substantial experience building and operating shared platform services at enterprise scale • Experience operating production infrastructure with real SLOs and on-call ownership • Experience in one or more of API gateways/traffic enforcement, policy-as-code and authorization, workload identity/zero-trust, observability and telemetry pipelines, or audit/compliance data platforms • Strong distributed-systems and platform-engineering fundamentals • Strong programming skills in Python and/or Go; TypeScript/Java an asset • Cloud-native architecture across AWS and Azure, including containers/Kubernetes, service mesh, and Infrastructure as Code (CDK, Terraform, CloudFormation/ARM) • Robust CI/CD, GitOps, and DevSecOps practice • Experience with Git-based workflows (Bitbucket/GitHub), Jira, and Confluence • Capability-specific depth in policy/authorization, identity/zero-trust, observability/audit, gateway/guardrails, or registry/portal technologies • Working knowledge of GenAI platform patterns, including LLM/AI gateways, RAG, agentic patterns, foundation models, embeddings, and guardrails • Familiarity with Bedrock, Azure OpenAI, SageMaker, MLflow, LangChain, and/or LlamaIndex • Grounding in Responsible AI, AI/data governance, privacy, cloud security, and IAM applied to AI workloads • Participation in an on-call rotation for owned services

🏖️ Benefits

• Performance-based incentives • Discretionary bonuses • Health insurance • Tuition reimbursement • Accident insurance • Life insurance • Retirement savings plans • In-depth training and coaching • Manager support • Network-building opportunities • Tools and resources to reach new milestones • Reasonable accommodations for individuals with disabilities