Role Description

We are seeking an experienced Senior Data Engineer to join our AI team. In this role, you will lead the development and optimization of data infrastructure supporting our Agentic AI initiatives. You will collaborate with ML engineers, AI scientists, and product managers to architect, implement, and maintain robust data pipelines powering autonomous AI agents. As a senior member of the R&DS AI Innovation Program, you will help shape data strategy and ensure our data solutions scale to meet the demanding requirements of next‑generation AI systems.

Key Responsibilities

Mandatory

• Design, develop, and maintain scalable data pipelines and ETL processes supporting AI research and development.

• Design and maintain scalable data models (e.g., star schemas, feature‑ready datasets, semantic layers) for analytics, ML training, and agent workflows.

• Collaborate with AI scientists and engineers to gather data requirements and ensure availability and quality.

• Implement data governance and security measures to protect sensitive information.

• Establish observability, lineage tracking, and monitoring frameworks to detect anomalies, freshness issues, and operational failures.

• Implement data partitioning, indexing, and storage optimization techniques for large‑scale AI datasets.

• Monitor and troubleshoot data pipeline issues to ensure continuity and reliability.

• Stay current with emerging data engineering and AI technologies.

• Drive data platform reliability, scalability, and cost optimization across cloud‑based infrastructure.

Preferred

• Design and implement scalable, resilient data architectures for AI agent training, fine‑tuning, and inference workflows.

• Build streaming and event‑driven pipelines enabling real‑time agent feedback, telemetry, and adaptive learning.

• Develop and maintain high‑performance pipelines using modern orchestration frameworks to support real‑time agent interactions.

• Create specialized storage and retrieval systems for vector embeddings, knowledge graphs, and symbolic reasoning components.

• Implement automated data validation, schema testing, and quality checks ensuring reliable AI training datasets.

• Implement comprehensive monitoring and governance frameworks ensuring high‑quality training data and compliance with privacy regulations.

• Continuously optimize system performance with a focus on reducing latency for agent decision‑making.

Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field; advanced degree preferred.

Experience

• 5+ years of professional experience in data engineering, including at least 2 years focused on ML/AI data infrastructure.

Programming & Technologies

• Advanced proficiency in Python and Scala; experience with Rust, Go, Java, or Julia is valued.

• Expert‑level knowledge of SQL and NoSQL databases.

• Hands‑on experience with vector databases (e.g., Pinecone, Weaviate, Milvus).

• Proficiency with modern data orchestration platforms (e.g., Airflow 2.x).

Cloud & Infrastructure

• Extensive experience with at least one major cloud platform (AWS, Azure, or GCP).

• Expertise in containerization and orchestration (Docker, Kubernetes).

• Experience with Infrastructure as Code tooling (e.g., Terraform).

Data Processing

• Experience with distributed computing frameworks (Spark, Dask, Ray).

• Proficiency with streaming technologies (Kafka, Flink).

• Knowledge of modern data lakehouse architectures.

Preferred Qualifications

• Certifications in cloud platforms, big data technologies, engineering, or ML operations.

• Experience collaborating with ML engineers on CI/CD pipelines for data processing and model deployment.

• Working knowledge of ML frameworks (PyTorch, TensorFlow).

• Experience with feature stores and experiment‑tracking platforms.

• Understanding of LLM fine‑tuning data requirements and processing.

• Experience developing data systems for autonomous AI agents or agentic AI applications.

• Background in prompt engineering or retrieval‑augmented generation systems.

• Experience with semantic caching and efficient storage/retrieval of AI‑generated artifacts.

• Familiarity with LLM evaluation metrics and benchmarking frameworks.

• Expertise in hybrid architectures combining traditional databases with vector stores.

• Experience with RAG systems and related data pipelines.

• Knowledge of RLHF data workflows.

• Experience mentoring junior engineers, establishing best practices, and contributing to architectural decisions.