Role Overview

We are seeking a highly skilled AI Data Engineer with a strong foundation in data engineering and applied AI/ML, capable of exploring Copilot/agentic workflows and translating business challenges into practical AI-driven solutions. The ideal candidate will have exposure to pharmaceutical R&D and regulatory processes, with the ability to design scalable, compliant, and production-grade AI data pipelines.

Key Responsibilities

• Design, build, and optimize scalable data pipelines for AI/ML use cases using modern cloud platforms.
• Develop and integrate AI/ML solutions, including LLM-based Copilot and agentic workflows.
• Identify and implement practical AI use cases aligned with Pharma R&D and regulatory functions.
• Collaborate with cross-functional teams (Data Science, R&D, Regulatory Affairs, IT) to translate business needs into technical solutions.
• Enable data ingestion, transformation, and governance across structured and unstructured datasets.
• Implement AI orchestration frameworks for intelligent automation (agents, Co-pilots, workflow automation).
• Ensure data quality, lineage, security, and compliance with regulatory standards (e.g., GxP, FDA).
• Support regulatory publishing systems or similar applications for structured documentation and submissions.
• Build reusable data models and feature engineering pipelines for AI consumption.
• Optimize performance and scalability of AI data platforms in cloud environments.

Required Skills / Competencies

Primary Skills

• Strong experience in Data Engineering (ETL/ELT, data pipelines, data modeling)
• Hands-on with Python / SQL / PySpark
• Experience with Cloud Platforms: Azure / AWS / GCP
• Exposure to AI/ML pipelines and LLM ecosystems
• Practical experience with Copilot frameworks / Generative AI / Agent-based architectures
• Understanding of data architecture, data lakes, and warehousing

AI / Advanced Skills

• Experience with LLM orchestration tools (e.g., Azure OpenAI, LangChain, Semantic Kernel, etc.)
• Building agent-based workflows / autonomous AI systems
• Prompt engineering and AI solution design
• Integration of AI models into enterprise workflows

Domain Expertise (Preferred)

• Experience in Pharma / Life Sciences R&D
• Understanding of clinical data, drug discovery, or research data workflows
• Exposure to Regulatory Affairs processes
• Familiarity with Regulatory Publishing tools (e.g., eCTD, submission systems)
• Knowledge of GxP, FDA, and compliance standards

Nice to Have

• Experience in Snowflake, Databricks, or Big Data ecosystems
• Knowledge of MLOps / DataOps practices
• Experience working in global delivery/offshore models
• Certifications in Cloud / AI / Data Engineering