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# Principal Software Engineer, AI Data Engineering

- **Company:** Philip Morris International Inc.
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Engineering, Encodings, Continuous Integration, Information Engineering, Data Governance, Distributed Systems, Software Engineering, Enterprise Software Applications, Indexer, Event Driven Architecture, Information Technology, Low Latency, Data Pipelines
- **Published:** November 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pwofb882k0

## About the Role

* Degree in Computer Science, IT, Software Engineering or a related discipline.
 * Enterprise software engineering experience in cloud-native and distributed systems.
 * Hands-on enterprise AI data engineering, grounding and retrieval experience.
 * Knowledge of data pipelines, contracts, schemas, lineage, provenance, metadata and quality controls.
 * Experience with ingestion, indexing, chunking, embedding, retrieval and refresh patterns.
 * Ability to assess relevance, freshness, scalability, latency, cost and access-control trade-offs.
 * Experience with batch and streaming architectures, observability and recoverability.
 * Strong secure software development, CI/CD, testing and production-readiness foundations.
 * Proven technical leadership through standards, reviews, supplier assurance and remediation.
 * Strong communication, cross-team influence, mentoring and documentation skills.

Nice-to-have

 * Advanced study in AI/ML, distributed systems or cloud/platform engineering.

## Description

Join our AI Delivery & Engineering team and provide principal-level technical leadership for reusable enterprise AI data and grounding capabilities. You'll help ensure AI solutions use secure, scalable, reliable and well-governed data foundations.

Impact You'll Own

 * Define data engineering standards for reusable AI data and grounding capabilities.
 * Strengthen solution quality through reviews of supplier designs, code, schemas, transformations and evidence.
 * Guide decisions on data sources, retrieval relevance, freshness, quality, cost and latency.
 * Improve traceability, permissions, lifecycle controls and readiness across shared assets and AI solutions.
 * Align data standards, dependencies and ownership boundaries across engineering and data teams.

What You'll Do

 * Define standards for data contracts, ingestion, transformation, indexing, grounding and refresh patterns.
 * Review supplier pipelines, code, schemas, retrieval configurations, lineage and quality evidence.
 * Assess source suitability, freshness, retrieval relevance and downstream AI behavior.
 * Set expectations for permissions, access controls, retention and deletion across sources, indexes and caches.
 * Partner with Quality and Testing to assess completeness, validity, provenance and reproducibility.
 * Review readiness for schema changes, refresh failures, recoverability and operational handover.
 * Turn findings and incidents into improved standards, guidance and reference patterns.