Principal ML & AI Engineer

HERE Technologies•United States Home Office,

1d•$195,000 - $210,000•Remote

About The Position

The Responsible AI Engineering team is part of the AI & Analytics Infrastructure team, which is building the agentic AI infrastructure and the governance around it. As a member of this team, you will become a subject matter expert in Agentic AI while working across security, OSS compliance, and AI governance domains. You will work with a global team to build tooling, develop technical guardrails, improve compliance processes, and establish best practices for AI-enabled products and services. Every team in the company wants to adopt AI, and every team operating its own pipelines, serving stack, and monitoring multiplies reliability, security, and cost risk. This role exists to solve that. You will own the MLOps foundation behind the shared AI platform: repeatable delivery pipelines, infrastructure automation, model and prompt lifecycle management, production serving, observability, evaluation, and cost-efficient operations. Instead of each team inventing its own path to production, they build and operate on the paved road you create. This is a hands-on principal engineering role. You will set technical direction, write production code and infrastructure-as-code, establish operational standards, and embed with consuming teams to move AI workloads from prototype to dependable production services. You will work closely with the platform architect and security, identity, data, and application teams in a security-sensitive enterprise environment.

Requirements

Extensive experience building and operating production MLOps or AI platform capabilities in an enterprise environment.
Strong software engineering skills, especially Python, with a track record of owning systems from design through production operations.
Deep experience with CI/CD, GitOps, infrastructure-as-code, containerized workloads, and automated environment promotion.
Strong AWS-native infrastructure experience, including Bedrock, EC2, IAM, load balancing, networking, and secrets management; experience with Kubernetes and Terraform or equivalent technologies.
Production model-serving experience, including endpoint management, autoscaling, resilience, rollout and rollback, performance testing, and capacity planning.
Observability and site reliability depth: metrics, dashboards, alerting, distributed tracing, audit logging, service-level objectives, incident response, and root-cause analysis.
Experience with model, prompt, dataset, and artifact versioning; registries; lineage; reproducibility; and release governance.
Experience implementing automated AI evaluation and regression gates, including quality, latency, reliability, and cost signals.
Operational experience with RAG pipelines, embedding workflows, vector stores, ingestion, indexing, and data freshness controls.
Understanding of multi-tenant isolation, identity and secrets integration, policy enforcement, and cost attribution for shared platforms.
Principal-level technical leadership: setting direction across teams, simplifying ambiguous problems, mentoring engineers, and influencing architecture without relying on organizational authority.
The customer-facing instincts to run discovery, explain trade-offs to technical and executive audiences, and earn trust in security-sensitive organizations.

Responsibilities

Own the MLOps foundation behind the shared AI platform: repeatable delivery pipelines, infrastructure automation, model and prompt lifecycle management, production serving, observability, evaluation, and cost-efficient operations.
Set technical direction, write production code and infrastructure-as-code, establish operational standards, and embed with consuming teams to move AI workloads from prototype to dependable production services.
Work closely with the platform architect and security, identity, data, and application teams in a security-sensitive enterprise environment.
Build tooling, develop technical guardrails, improve compliance processes, and establish best practices for AI-enabled products and services.
Become a subject matter expert in Agentic AI while working across security, OSS compliance, and AI governance domains.