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# Lead AI & Data Engineer

- **Company:** Dassault Systems Enovia Corp.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $135,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Audit Trail, Batch Processing, Clinical Data Management, Cloud Computing, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Graph Database, Job Scheduling, Python (Programming Language), Meta-Data Management, Search Technologies, SQL Databases, Enterprise Data Management, Data Processing, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Agentic-AI, Git, Data Layers, AI Platforms, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** October 20, 2026
- **Apply:** https://talentacquisition.3ds.com/careersection/qa/jobapply.ftl?job=550610&lang=en&src=CWS-10080

## About the Role

* 7+ years of experience in data engineering or data science, with a strong foundation in enterprise data architecture, including at least 2 years of hands-on experience in AI and data engineering.
 * Bachelor's degree in a technical or scientific field, such as Data Engineering, Data Science, Computer Science, or similar.
 * Hands-on experience developing AI-native solutions using LLMs, RAG, AI agents, and agent orchestration patterns.
 * Skilled in integrating AI/ML pipelines into production-grade data infrastructure with experience in API and MCP standards and integration, including model access controls, context management, auditability, security, compliance monitoring, and lifecycle management
 * Experience implementing AI governance and guardrails, including access controls, data privacy, auditability, and policy enforcement across AI services and agents
 * Proficiency in Python, SQL, and modern data engineering practices, including CI/CD and Git-based development.
 * Hands-on experience with tools and concepts such as Airflow, CDC, batch processing, and job scheduling.
 * Strong problem-solving, communication, and collaboration skills, with the ability to troubleshoot complex issues and to translate business requirements into secure, scalable, production-ready solutions.
 * Experience with healthcare or clinical trial data is not required, but interest to learn and understand how these data improves Life Sciences is paramount., * Experience with knowledge graphs, advanced vector search, and retrieval optimization techniques.
 * Familiarity with clinical research data standards and formats such as CDISC SDTM and ADaM.
 * Experience with AI evaluation frameworks, benchmarking, and continuous monitoring of AI solution performance.
 * Experience optimizing cloud infrastructure, AI workloads, and data processing costs.
 * Familiarity with semantic layers, metadata-driven architectures, and automated data cataloging.

## Description

The ideal candidate combines deep data engineering and AI-native development expertise to build scalable, secure, and reliable AI-enabled data solutions. You will drive technical architecture, data pipeline development, AI integration, and engineering best practices, translating complex business and clinical research requirements into production-ready solutions. You will work closely with product management, data science, platform engineering, and clinical experts, to deliver innovative capabilities that enable intelligent workflows and data-driven decision-making., * Engineer scalable, automated data pipelines and AI-powered solutions using modern AI technologies, vector databases, and automation, applying CI/CD practices and tools such as Python, SQL, and Git.

 * Design, develop, and deploy production-grade AI-powered solutions leveraging LLMs, RAG, and agentic workflows.
 * Implement and maintain integrations via API and MCP that connect applications and agents to enterprise data, tools, and skills in line with governance, security and compliance controls.
 * Implement agent orchestration and workflow patterns using frameworks such as LangGraph, LangChain, or equivalent technologies.
 * Develop agent-driven metadata management solutions to maintain data catalogs, business glossaries, lineage documentation, and governance policies.
 * Apply advanced skills in data architecture, data science engineering, data modeling, and data quality using modern cloud-native technologies.
 * Document and communicate technical work clearly to stakeholders at all levels, both technical and non-technical.
 * Collaborate effectively in Agile environments and cross-functional teams