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ML Engineer-Data Science

Clinvia LLC Remote, US
Full-time Posted 27 days ago

Responsibilities

  • check_circle Design and develop AI Agents and AI Copilots for Statistical Programming, and Biostatistics functions.
  • check_circle Build intelligent workflows to automate SAS programming, clinical reporting, data validation, and documentation tasks.
  • check_circle Develop and integrate LLM-based solutions to assist programmers in code generation, review, debugging, and optimization.
  • check_circle Collaborate with Data Science team and Business Stakeholders to gather requirements.
  • check_circle Create AI solutions for SDTM, ADaM, TLF generation, clinical study reports, and regulatory submission support.
  • check_circle Develop Retrieval-Augmented Generation (RAG) systems using clinical and regulatory knowledge repositories.
  • check_circle Integrate AI agents with SAS, Python, clinical databases, and enterprise applications.
  • check_circle Ensure compliance with GxP, FDA, EMA, and data privacy regulations.
  • check_circle Monitor, evaluate, and improve AI agent performance, accuracy, and reliability.
  • check_circle Prepare technical documentation, validation plans, and deployment artifacts.

Basic qualifications

  • Bachelor's or Master's degree in Statistics, Computer Science, Data Science, Statistics or a related field.
  • 3+ years of experience in AI/ML development, software development, or pharmaceutical analytics.
  • Strong programming skills in Python.
  • Working knowledge of SAS Programming
  • Experience with Large Language Models (LLMs), Generative AI, and AI Agent frameworks.
  • Knowledge of prompt engineering, RAG architecture, vector databases, and AI orchestration frameworks.
  • Strong analytical and problem-solving skills.

Preferred qualifications

  • Exposure to Biostatistics and Statistical Programming projects.
  • Experience with AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies.
  • Knowledge of cloud platforms such as AWS, Azure, or GCP.
  • Familiarity with MLOps and AI governance practices.
  • ML Engineering: 3 years (Preferred)

Tags & Focus Areas

Contract Remote Ai Machine Learning Data Science Generative Ai

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