Hiring Machine Learning Engineer

3 years

0 Lacs

Posted:3 days ago| Platform: GlassDoor logo

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Work Mode

On-site

Job Type

Part Time

Job Description

Position: Machine Learning Engineer
Team Size: 6–8 Members
Location: Trivandrum
Experience Level: 3+ Years

Role Overview

We are seeking a highly skilled Machine Learning Engineer with expertise in NLP, OCR, and clinical data processing to join our AI/ML team. The ideal candidate will develop and deploy ML models for healthcare applications, focusing on clinical source data extraction, terminology recognition, and automated discrepancy detection. This role offers the opportunity to work on cutting-edge solutions in clinical data management while ensuring compliance with regulatory standards.

Key Responsibilities

  • Model Development: Design, train, and deploy ML models for OCR, NLP, and structured data extraction from clinical source documents (PDFs, scanned forms, EHR exports).
  • Terminology Recognition: Implement medical terminology mapping and entity recognition (SNOMED CT, MedDRA, LOINC).
  • Automation: Develop algorithms for discrepancy detection between source data and EDC.
  • Query Generation: Build context-aware query generation modules to support clinical data management workflows.
  • Collaboration: Work closely with data engineers and backend developers to integrate ML pipelines into production systems.
  • Compliance: Ensure all ML solutions follow HIPAA/GDPR guidelines and privacy-preserving ML practices.

Qualifications

  • Master’s/PhD in Computer Science, Data Science, or a related field.
  • 3+ years of hands-on experience in ML/NLP, preferably in healthcare or clinical data.
  • Strong expertise in LLMs, prompt engineering, and hyperparameter tuning.
  • Proficiency in PyTorch/TensorFlow, Hugging Face Transformers, and OCR libraries (e.g., Tesseract, Amazon Textract).
  • Experience with AWS Bedrock, SageMaker (AgentCore is a strong plus).
  • Knowledge of HIPAA/GDPR compliance and privacy-preserving ML approaches.

Job Type: Permanent

Work Location: In person

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