Lead/Senior Research Engineer

Job Description

The Digital Supply Chain Group at the Digital Manufacturing Division, ARTC, is seeking a Lead/Senior Data Engineer with strong expertise in data pipelines, transformation, analytics, Gen-AI enablement, and workflow automation. This role supports ongoing and new AI research efforts focused on supply chain analysis, intelligent automation, and decision-support systems. The successful candidate will play a pivotal role in preparing clean, structured, and timely data; developing automation-ready data services; and enabling AI-driven and Gen-AI-enabled solutions for real-world supply chain challenges in FMCG, Med-tech, manufacturing, aerospace, energy, and semiconductor sectors.

Key Responsibilities

• Build and Maintain Data Infrastructure
  
  • Design, implement, and maintain scalable ELT/ETL pipelines across diverse data sources (SAP, MES, WMS, ERP, IoT, etc.)
  • Develop automated data ingestion and transformation processes using modern tools (e.g., Airflow, dbt, Kafka, etc.)
• Data Modeling & Analytics Support
  • Perform data wrangling and preprocessing tailored for ML/AI model training and simulation environments
  • Work with AI scientists to prepare datasets for time-series forecasting, optimization models, simulation environments, and Gen-AI applications such as retrieval-augmented generation, knowledge assistants, and automated reporting
  • Structure and curate domain knowledge, metadata, and enterprise data assets to support reliable Gen-AI workflows, including prompt-ready datasets, semantic search, and knowledge-base development

• Collaborate Across Domains
  • Liaise with domain experts, supply chain analysts, and software developers to understand operational data needs
  • Serve as the bridge between raw data and AI solution pipelines
  • Translate business and research requirements into automated workflows that connect data ingestion, analytics, model outputs, user interfaces, and operational decision processes
• Maintain Data Quality & Governance
  • Implement checks, logging, and alerts to ensure high data reliability and traceability
  • Ensure alignment with FAIR data principles and secure data handling practices
  • Establish data lineage, validation, access control, and monitoring practices required for production-grade AI and Gen-AI solutions

• Tooling and Deployment
  • Develop containerized and cloud-compatible data solutions (e.g., using Docker, Kubernetes, AWS, Azure)
  • Contribute to end-to-end solution integration with dashboards, workflow automation platforms, digital twin systems, AI copilots, or decision-support applications
  • Develop reusable APIs, services, and automation components that enable scalable deployment of analytics, Gen-AI, and workflow solutions across research and industry projects

Job Requirements

• Bachelor's/Master's degree in Computer Science, Data Engineering, Information Systems, or a related field
• Strong proficiency in Python and SQL; familiarity with PySpark, Pandas, or Dask is a plus
• Proven experience building data pipelines in cloud or hybrid environments
• Relevant hands-on experience in Gen-AI solution enablement, including RAG pipelines, vector databases, semantic search, knowledge-base preparation, prompt engineering support, or LLM application integration
• Experience designing or implementing workflow automation solutions using APIs, orchestration tools, low-code/no-code platforms, robotic process automation, or enterprise automation frameworks
• Familiarity with supply chain data systems (SAP, ERP, MES) and industry-specific data schemas is highly preferred
• Hands-on experience with data lakes, data warehousing, or streaming architectures
• Excellent interpersonal and communication skills; ability to work in cross-functional R&D teams
• Bonus: Familiarity with supply chain KPIs, AI/ML workflows, Gen-AI application patterns, agentic workflows, and applied industrial analytics is advantageous