Job Description
Design, develop, and deploy machine learning and deep learning models for classification, regression, clustering, forecasting, anomaly detection, and predictive analytics use cases.
- Build and optimize feature engineering pipelines and data preprocessing workflows for structured and unstructured datasets.
- Perform exploratory data analysis (EDA) to identify trends, patterns, and actionable insights from large-scale healthcare and business datasets.
- Develop and evaluate models using appropriate metrics, cross-validation, and experimentation frameworks.
- Implement NLP solutions for text classification, entity extraction, summarization, and information retrieval.
- Design and fine-tune Large Language Models (LLMs) and transformer-based architectures for domain-specific applications.
- Apply transfer learning and prompt engineering techniques to adapt foundation models to business and healthcare use cases.
- Build AI-driven document intelligence solutions using models such as LayoutLM, Donut, and Table Transformers for key-value extraction and document understanding.
- Support MLOps and model deployment, including Docker-based packaging, cloud deployment, monitoring, and performance optimization.
- Collaborate with product, engineering, and business teams to translate business problems into AI/ML and GenAI solutions.
- Research and experiment with emerging AI technologies, including RAG (Retrieval-Augmented Generation), LLM alignment techniques (RLHF, DPO, PPO, KTO), and model optimization methods.
Requirements
- 5+ years of hands-on experience in AI/ML model development and deployment.
- Strong understanding of machine learning, deep learning, neural network architectures, training methodologies, and optimization algorithms.
- Proficiency in Python and AI/ML libraries such as PyTorch, TensorFlow, Scikit-Learn, Pandas, and NumPy.
- Experience with NLP libraries such as Hugging Face Transformers, spaCy, and NLTK.
- Hands-on experience with LLM fine-tuning, prompt engineering, and transformer-based models.
- Experience with SQL and/or NoSQL databases and data manipulation at scale.
- Knowledge of cloud platforms (AWS/Azure), Docker, and ML deployment workflows.
- Strong analytical, problem-solving, and communication skills, with the ability to explain complex AI concepts to technical and non-technical stakeholders.
Preferred Skills
- Experience with RAG architectures, vector databases, embeddings, and semantic search.
- Familiarity with LLM alignment techniques such as RLHF, DPO, PPO, and KTO.
- Experience with computer vision or document AI models for OCR and document understanding.
- Knowledge of Airflow or other workflow orchestration tools.
- Experience working with HIPAA, PHI, PII, or GDPR-compliant systems.
- Familiarity with Linux, Git, Jupyter Notebooks, and Agile development practices.
- Experience with distributed computing and scalable AI infrastructure is a strong advantage.
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