Senior AI Platform Integration Specialist

BP•Denver, CO

13d•Hybrid

About The Position

bpx energy is building an enterprise AI capability that requires clear platform roles, reusable integration patterns, and a focused approach to transforming project-based implementations into reusable enterprise capabilities. The Senior AI Platform Integration Specialist will define how AI/ML capabilities connect into the broader enterprise ecosystem, including Databricks, AWS, Snowflake, Palantir, APIs, applications, BI tools, and operational workflows. This role is essential to ensuring that AI capabilities are not built as isolated point solutions or made into a single platform by default. The role will help define what belongs where, how AI outputs should be consumed, what integration standards are , and how platforms work together safely and optimally. Because Palantir is a strategic enterprise workflow and integration layer, this role will help shape the long-term Palantir platform strategy under the AI/ML Engineering and Architecture Manager. The role will define how Palantir consumes AI/ML outputs while maintaining appropriate boundaries between AI engineering, workflow execution, ontology ownership, and business value delivery.

Requirements

Bachelor’s degree in engineering, computer science, information systems, or related field, or equivalent work experience.
Validated experience designing enterprise integration patterns across AI/ML platforms, data platforms, APIs, applications, and workflow systems.
Strong understanding of where AI/ML capabilities should be engineered, where data should be governed, where workflows should be executed, and how outputs should be consumed.
Experience designing technical patterns for model outputs, recommendations, alerts, scores, embeddings, agents, or AI services to be consumed by business-facing systems.
Practical experience with API design, event-driven architecture, service integration, identity/access controls, data contracts, lineage, observability, and auditability.
Ability to create reusable reference architectures and prevent one-off, tailored integrations.
Experience working across technical teams, product teams, platform owners, enterprise architecture, cybersecurity, and business collaborators.
Ability to evaluate platform fit-for-purpose and define clear boundaries between platforms such as Databricks, AWS, Snowflake, Palantir, BI tools, and custom applications.
Ability to translate integration strategy into implementable standards, patterns, and decision frameworks.
Proven ability to govern or architect integrations with enterprise workflow, ontology, or operational decision platforms such as Palantir or equivalent.
Ability to define how AI/ML outputs should be consumed by Palantir while maintaining appropriate boundaries between AI engineering, workflow execution, ontology ownership, and business value delivery.
Strong understanding of Palantir platform interoperability, API-based integration, data lineage, security, and operational controls in a multi-platform ecosystem.

Nice To Haves

Experience with Palantir Foundry/AIP, especially integration patterns, ontology considerations, workflows, and external model/API consumption.
Experience with Databricks, Snowflake, AWS, APIs, event streams, and enterprise application integration.
Experience defining platform decision rules: what belongs where, what should be reused, and what requires governance gates.
Experience in industrial, operational, asset-intensive, regulated, or high-consequence environments.
Experience designing human-in-the-loop patterns, operational decisioning workflows, or AI-enabled advisory systems.
Experience translating enterprise architecture into practical enablement materials for delivery teams.

Responsibilities

Partner with data, BI, cybersecurity, enterprise architecture, platform, and business teams to align AI/ML integration with enterprise standards.
Ensure integrations are designed for security, lineage, observability, auditability, reliability, and operational support.
Define enterprise integration patterns across Databricks, AWS, Snowflake, Palantir, APIs, applications, workflow platforms, and BI tools.
With Enterprise Architecture, co-create reference architectures for AI/ML capabilities that need to be consumed by business-facing systems or operational workflows.
Define how model outputs, recommendations, alerts, scores, embeddings, agents, or AI services should be exposed, consumed, governed, and monitored.
Establish platform decision rules that clarify what belongs in Databricks/AWS, what belongs in Snowflake, what belongs in Palantir, and what should be exposed through APIs or enterprise services.
Partner with Palantir platform and delivery teams to define approved patterns for AI output consumption, API integration, observability, security, and auditability.
Help prevent one-off or bespoke integrations by creating reusable patterns, technical guardrails, and design standards.
Support federated intake and enablement by helping domain teams shape ideas into reusable, supportable architecture patterns.
Support long-term platform strategy for Palantir as an enterprise workflow and AI-consumption layer.