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This position was posted on April 14, 2026 and is likely no longer accepting applications. We've kept it here for historical reference. Check out the similar jobs below!

Job Overview
Location
Remote, India
Job Type
Full-time
Category
Software Engineering
Date Posted
April 14, 2026
Full Job Description
📋 Description
- • AI/ML Engineer II at Precision AQ, LLC is a hands-on technical role focused on designing, developing, deploying, and scaling production-grade AI/ML solutions that directly support oncology access, analytics, and AI-enabled productized services.
- • Day-to-day responsibilities include developing and optimizing machine learning, deep learning, and Generative AI models (including LLMs), building and maintaining MLOps pipelines, deploying AI applications in cloud environments (Azure, AWS, GCP), monitoring model performance in production, and collaborating with product, engineering, and data science teams to translate business needs into technical AI systems.
- • The role sits within Precision AQ’s expanding Product Solutions team in India, working closely with cross-functional teams to deliver robust, scalable AI systems that power real-world healthcare and life sciences applications.
- • The engineer will gain deep expertise in production ML systems, LLMs, MLOps, and healthcare AI applications while contributing to internal best practices, documenting workflows, and improving scalability and reliability of existing AI infrastructure.
Skills & Technologies
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About Precision AQ, LLC
Precision AQ, LLC is a global commercialization partner that helps biopharmaceutical companies bring therapies to market faster. It combines data science, strategic consulting, and field solutions to optimize market access, pricing, reimbursement, and patient services. Operating across the United States, Europe, and Asia, the firm supports product launches and lifecycle management by integrating real-world evidence, health economics, and stakeholder engagement, enabling clients to navigate complex healthcare ecosystems and improve patient outcomes.
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