Role Synopsis

bpx energy is building an enterprise AI capability that can scale safely and deliver real operational value. We are seeking a AI/ML Engineering and Architecture Manager to own how AI is engineered, deployed,operated, and governed across the company.This is a deeply technical AI engineering leadership role, not a traditional software or application engineering position. Successrequireshands‑onexperience operating production AI/ML systems at scale, includingModelOpsMLOps, platform engineering, and runtime reliability.

For 2026, the role will lead centralized AI/ML engineering with federated intake and enablement,establishingthe standards, platforms, and “paved roads”requiredto move fast without creating risk. As these foundations mature, the role will define and enable a clear pathway tofederatedAI/ML engineering for domain teams (targeted for 2027, based on readiness).

This role shapes AI capabilities that supportenterprise‑criticaloperations,system‑leveldecisioning, and scalable automation across bpx energy, where engineering rigor and operational reliability have material business impact. “Federated” refers to the architectural and operating model design, enabling future distributed delivery once enterprise standards and governance are in place; engineering executionremainscentralized in 2026.

WhatYou’llDo

• Define and own bpx's enterprise AI/ML engineering architecture, standards, and operating model

• Operate and evolve modern AI/ML platforms (e.g., Databricks or equivalent) forproduction‑gradeuse

• OwnModelOpsMLOps, including deployment automation, monitoring, drift detection, evaluation, and lifecycle management

• Create the paved road for AI delivery: reusable patterns, approved integrations, tiered access controls, and observable operations

• Embed security, governance, and audit-ability directly into engineering workflows

• Enable domain teams through federated intake and enablement, while preventing uncontrolled “wild west” development

• Partner closely with data, BI, security, and architecture leaders to enforce clear platform roles and interoperability standards

• Build and lead a small,high‑impactplatform and enablement team

• Own enterprise AI/ML platform engineering and technical enablement, includingDatabricks as the primary AI/ML engineering platform, and technical oversight of Palantir’s integration with AI/ML services to ensure fit‑for‑purposeexecution, platform discipline, and adherence to enterprise standards.

• Palantir value delivery, workflow ownership, and ontology ownershipremainoutside this role;this role owns the technical patterns, AI integration standards, and guardrails that determinehow and when Palantir consumes AI outputs.

What This Role Is Not

• Not a traditional software engineering or application platform leadership role

• Not focused on building individual AI use cases or owning business value delivery

• Not a “hands‑off” architecture governance position

Required Qualifications

• 5 years ofdemonstratedexperience operating production AI/ML systems in large or sophisticated environments

• Prior, multi-yearownership ofModelOpsMLOps, including deployment, monitoring, evaluation, retraining, and retirement

• 5yearshands‑onexperience with modern AI/ML platforms (e.g., Databricks, Snowflake, Palantir or equivalent), beyond experimentation

• Proven track record to design andoperateAI engineering systems that differ materially from traditionalapplicationor data platforms

• Strong fundamentals in security, controls, and operational reliability (IAM/RBAC, audit logging, incident management)

• Candidates whose experience is limited to traditional software engineering, application platforms, or data engineering—without direct responsibility for AI/ML operations at scale—will not be considered.

Why This Role Matters

This roleestablishesthe engineering system that makes enterprise AI possible — enabling speed with guardrails, reducing duplication and risk, and ensuring bpx energy can scale AI responsibly over time.