Healthcare LLM / NLP Engineer

Job Brief:

About the Role Bacancy Technology is hiring a Healthcare LLM / NLP Engineer to build natural language processing and large language model solutions that extract actionable insights from unstructured clinical text. Over 80% of healthcare data is unstructured — clinical notes, discharge summaries, radiology reports, and pathology findings. You will develop NLP pipelines that transform this data into structured, computable formats for clinical decision support, automated coding, and research analytics.

Role: Healthcare Interoperability

Experience: 4+Years

Position Open: 1

Job Location: Iselin Nj

Work from Home Available: Not Available

Salary: Not a constraint for the right candidate

Roles and Responsibility:

Design and implement clinical NLP pipelines for extracting medical entities, relationships, and assertions from unstructured clinical text
Fine-tune and deploy healthcare-specific LLMs (BioBERT, ClinicalBERT, Med-PaLM) for clinical text classification, summarization, and question answering
Build automated medical coding systems that map clinical narratives to ICD-10, CPT, and SNOMED CT codes
Develop clinical trial matching algorithms that parse eligibility criteria and match patients from EHR data
Implement named entity recognition (NER) for medications, diagnoses, procedures, and lab values from clinical documents
Design de-identification NLP systems to remove PHI from clinical text for research and secondary use
Build semantic search and knowledge retrieval systems over clinical document repositories
Validate NLP model performance against clinical gold standards and measure inter- annotator agreement

Skill Requirement:

Master’s or PhD in Computer Science, Computational Linguistics, Biomedical Informatics, or related field
4+ years of NLP engineering experience, with at least 2 years focused on clinical or biomedical text
Deep expertise in transformer-based models and fine-tuning on healthcare datasets
Strong knowledge of medical terminologies (UMLS, SNOMED CT, ICD-10, RxNorm)
Experience with clinical NLP frameworks (MedSpaCy, cTAKES, MetaMap, Amazon Comprehend Medical)
Proficiency in Python and NLP libraries (spaCy, Hugging Face, NLTK, Stanford NLP)
Understanding of HIPAA requirements for handling PHI in NLP training datasets
Published research or demonstrated expertise in biomedical NLP is strongly preferred

Nice to have

NLP Frameworks spaCy, Hugging Face Transformers, MedSpaCy, cTAKES, MetaMap, SciSpaCy
LLM Technologies BERT, BioBERT, ClinicalBERT, GPT-4, Med-PaLM, fine-tuning, few-shot learning
Medical Terminologies UMLS, SNOMED CT, ICD-10-CM, CPT, RxNorm, LOINC, MeSH
Data Processing Python, Pandas, Apache Spark NLP, annotation tools (Prodigy, brat, Label Studio)
Cloud ML AWS Comprehend Medical, Azure Text Analytics for Health, Google Healthcare NL API
Evaluation Precision/Recall/F1, inter-annotator agreement (Cohen’s Kappa), clinical validation protocols

Qualification: B.Tech/B.E/MCA

Office Timings: 10.00 AM to 7.00 PM

Perks and Benefits: