Senior AI/ML Platform Engineer

Job Family Group:

IT&S Group

Job Description:

bpx energy, a major oil and gas producer in the United States, demonstrates its expertise in unconventional gas, including shale, to deliver hydrocarbon production and technical knowledge worldwide. With operations in Texas and Louisiana, our US onshore business has become both a best-in-class oil and gas producer and a leader in reducing methane emissions. As part of BP, a global industry leader, we champion a high-energy, high-intensity environment built on accountability, collegiality, and empowerment.

Role Overview

bpx energy is building an enterprise AI capability that can scale safely and deliver real operational value. The Senior AI/ML Platform Engineer will help build and operate the technical foundation required to move AI/ML capabilities from project-based implementations into governed, observable, production-grade enterprise capabilities.

This is a hands-on platform engineering role focused on the systems, patterns, environments, controls, and automation required for production AI/ML delivery. The role will work across Palantir, Snowflake, Databricks, AWS, and related AI/ML services to create the “paved roads” that allow teams to be versatile and quick-moving.

This role will not focus on building one-off AI use cases. It is focused on making AI/ML engineering repeatable, reliable, secure, and scalable across the enterprise.

What You’ll Do

Build, operate, and evolve AI/ML platform capabilities across Palantir, Databricks, AWS, MLflow, model registries, model serving, feature management, vector stores, and related services.

• Create reusable platform patterns for model development, deployment, serving, monitoring, access controls, and production support.

• Implement CI/CD, infrastructure automation, environment management, secrets management, access controls, and deployment templates for AI/ML workloads.

• Partner with security, infrastructure, data, and enterprise architecture teams to ensure AI/ML platforms are secure, observable, auditable, and operationally reliable.

• Support batch, real-time, streaming, and API-based model deployment patterns.

• Establish standard engineering patterns for experiments, notebooks, jobs, pipelines, model serving, and production promotion.

• Help define platform usage standards, tiered access models, cost controls, observability requirements, and operational support patterns.

• Ensure AI/ML workloads are designed for reliability, scalability, performance, maintainability, and governance.

• Support future federated AI/ML engineering by creating reusable templates, reference architectures, and enablement materials for domain teams.