Key Responsibilities:

• The ability to design and develop ML, Gen AI, NLP, LLM Models for AI data pipelines. Model components will include data ingestion, preprocessing, Retrieval Augmented Generation (RAG), NLP/LLM model development, fine-tuning and prompt engineering.

• Design and develop of predictive and generative AI models for clinical use cases like summarizing clinical notes, chatbot assistants, emergency admissions' risk reduction, etc.

• Analyze large, complex healthcare datasets including electronic health records (EHR) and claims data

• Develop statistical models for patient risk stratification, treatment optimization, population health management, and revenue cycle optimization

• Build models for clinical decision support, patient outcome prediction, care quality improvement, and revenue cycle optimization

• Create and maintain automated data pipelines for real-time analytics and reporting

• Work with healthcare data standards (HL7 FHIR, ICD-10, CPT, SNOMED CT) and ensure regulatory compliance

• Work with product/business, engineering to data driven innovations/features.

• Develop and deploy models in cloud environments while creating visualizations for stakeholders

• Present findings and recommendations to cross-functional teams including clinicians, product managers, and executives

Qualifications required:

• Bachelor's degree in data science, Statistics, Computer Science, Mathematics, or related quantitative field

• 5+ years of working engineering/product development experience of AIML models.
  Knowledge of ML Ops practices deploying and operationalizing AI models.

• 2 years of GenAI data science experience in production systems

• Familiarity with cloud platforms (Azure, AWS, GCP) and containerized deployments (Docker, Kubernetes).

• Demonstrated experience working with large datasets and statistical modeling

• Proficiency in Python or R for data analysis and machine learning

• Experience with SQL and database management systems

• Knowledge of machine learning frameworks such as scikit-learn, TensorFlow, PyTorch

• Familiarity with data visualization tools such as Tableau, Power BI, matplotlib, ggplot2

• Exposure to validation, deployment and tuning of models along with production/customer/user adoption of models.

• Strong foundation in statistics, hypothesis testing, and experimental design.

• Experience with supervised and unsupervised learning techniques.

• Knowledge of data preprocessing, feature engineering, and model validation.

• Understanding of A/B testing and causal inference methods.

• Experience with cloud platforms and big data technologies such as Spark, Hadoop

What You’ll Need to Be Successful (Required Skills):

• Large Language Model (LLM) Experience: At least 2+ years of hands-on experience working with pre-trained language models (Claude, Llama models , GPT, BERT, T5) including fine-tuning, prompt engineering, and model evaluation techniques.

• Generative AI Frameworks: Proficiency with generative AI libraries and frameworks such as Hugging Face Transformers, Lang Chain, OpenAI API, or similar platforms for building and deploying AI applications

• Prompt Engineering and Optimization: Experience designing, testing, and optimizing prompts for various use cases including text generation, summarization, classification, and conversational AI applications

• RAG Models and Vector Databases : Knowledge of vector similarity search, embedding models, and vector databases (Pinecone, we aviate, Chroma) for building retrieval-augmented generation (RAG) systems

• AI Model Evaluation: Experience with evaluation methodologies for generative models including BLEU scores, ROUGE metrics, human evaluation frameworks, and bias detection techniques

• Multi-modal AI Systems: Familiarity with multi-modal generative models combining text, images, and other data types, including experience with vision-language models and cross-modal applications

• AI Safety and Alignment: Understanding of responsible AI practices including content filtering, bias mitigation, hallucination detection, and techniques for ensuring AI outputs align with business requirements and ethical guidelines

• Agentic AI Systems: Knowledge of developing multi-agent systems for collaborative healthcare tasks

Education/ Certifications:

Bachelor’s or master’s degree in computer science, Data Science, Statistics, Engineering, or a related field.

Preferred Skills:

• At least 1 year of experience working with healthcare data or in healthcare IT environments

• Familiarity with electronic health record (EHR) systems and healthcare workflows

• Understanding of healthcare data privacy regulations (HIPAA, HITECH)

• Knowledge of clinical data standards and interoperability frameworks

• Knowledge of MLOps practices and model deployment pipelines

• Familiarity with natural language processing for clinical text analysis

• Experience with time series analysis for patient monitoring data

Why Join Us?

At Netsmart you’ll work on exciting challenges that shape the future of healthcare. You’ll have the opportunity to:

• Collaborate with talented professionals passionate about technology.

• Work in a supportive and inclusive environment where your growth is prioritized.

• Access professional development opportunities, including certifications and training.

• Enjoy a competitive compensation package and comprehensive benefits.