AI/ML Engineering and Architecture Manager

bp•Denver, CO

1d•$190,000 - $240,000•Hybrid

About The Position

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. Success requires hands‑on experience operating production AI/ML systems at scale, including ModelOps/MLOps, platform engineering, and runtime reliability. For 2026, the role will lead centralized AI/ML engineering with federated intake and enablement, establishing the standards, platforms, and “paved roads” required to move fast without creating risk. As these foundations mature, the role will define and enable a clear pathway to federated AI/ML engineering for domain teams (targeted for 2027, based on readiness). This role shapes AI capabilities that support enterprise‑critical operations, system‑level decisioning, 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 execution remains centralized in 2026.

Requirements

5 years of demonstrated experience operating production AI/ML systems in large or sophisticated environments
Prior, multi-year ownership of ModelOps/MLOps, including deployment, monitoring, evaluation, retraining, and retirement
5 years hands‑on experience with modern AI/ML platforms (e.g., Databricks, Snowflake, Palantir or equivalent), beyond experimentation
Proven track record to design and operate AI engineering systems that differ materially from traditional application or 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.

Nice To Haves

Commercial Acumen
Communication
Data Analysis
Data cleansing and transformation
Data domain knowledge
Data Integration
Data Management
Data Manipulation
Data Sourcing
Data strategy and governance
Data Structures and Algorithms (Inactive)
Data visualization and interpretation
Digital Security
Extract, transform and load
Group Problem Solving

Responsibilities

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) for production‑grade use
Own ModelOps/MLOps, 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‑impact platform and enablement team
Own enterprise AI/ML platform engineering and technical enablement, including Databricks as the primary AI/ML engineering platform, and technical oversight of Palantir’s integration with AI/ML services to ensure fit‑for‑purpose execution, platform discipline, and adherence to enterprise standards. Palantir value delivery, workflow ownership, and ontology ownership remain outside this role; this role owns the technical patterns, AI integration standards, and guardrails that determine how and when Palantir consumes AI outputs.