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 nextâgeneration AI systems.
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, featureâready 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 largeâscale 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 cloudâbased infrastructure.
Design and implement scalable, resilient data architectures for AI agent training, fineâtuning, and inference workflows.
Build streaming and eventâdriven pipelines enabling realâtime agent feedback, telemetry, and adaptive learning.
Develop and maintain highâperformance pipelines using modern orchestration frameworks to support realâtime 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 highâquality training data and compliance with privacy regulations.
Continuously optimize system performance with a focus on reducing latency for agent decisionâmaking.
Education
Bachelorâs or Masterâs 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.
Advanced proficiency in Python and Scala; experience with Rust, Go, Java, or Julia is valued.
Expertâlevel knowledge of SQL and NoSQL databases.
Handsâon experience with vector databases (e.g., Pinecone, Weaviate, Milvus).
Proficiency with modern data orchestration platforms (e.g., Airflow 2.x).
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).
Experience with distributed computing frameworks (Spark, Dask, Ray).
Proficiency with streaming technologies (Kafka, Flink).
Knowledge of modern data lakehouse architectures.
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 experimentâtracking platforms.
Understanding of LLM fineâtuning data requirements and processing.
Experience developing data systems for autonomous AI agents or agentic AI applications.
Background in prompt engineering or retrievalâaugmented generation systems.
Experience with semantic caching and efficient storage/retrieval of AIâgenerated 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.
IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com
IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.
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