OmniMD - Data Scientist (2-5 yrs)

Key Responsibilities:

- Model Development: Design, develop, and implement machine learning models and algorithms, with a particular focus on generative AI, LLaMA models, RAG, and vector databases for various applications.

- Data Analysis: Analyze and interpret complex datasets to identify patterns and trends that can be leveraged for ML/AI, generative AI, and RAG solutions.

- Deployment: Deploy machine learning models into production, ensuring scalability, reliability, and robustness, with an emphasis on generative AI, LLaMA models, RAG, and vector database integration.

- Research: Stay up-to-date with the latest developments in machine learning, AI, generative AI, LLaMA models, RAG, and vector databases, and apply new techniques and methodologies as appropriate.

- Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate ML/AI, generative AI, LLaMA models, RAG, and vector database solutions into products.

- Optimization: Optimize and fine-tune machine learning models, RAG techniques, LLaMA models, and vector database queries for performance, efficiency, and cost-effectiveness.

- Mentorship: Provide guidance and mentorship to junior ML/AI engineers and data scientists, particularly in the areas of generative AI, LLaMA models, RAG, and vector databases.

- Documentation: Maintain comprehensive documentation of models, algorithms, and processes, with a focus on generative AI, LLaMA models, RAG, and vector database techniques.

Qualifications:

- Education: Bachelor's, master's, or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related field.

- Experience: 2 to 5 years of experience in machine learning, AI, or a related field, with a minimum of 2 years of relevant experience in the US Healthcare Domain preferred.

Technical Skills :

- Proficiency in programming languages such as Python or Java.

- Strong understanding of machine learning frameworks (e. , TensorFlow, PyTorch, Keras).

- Extensive experience with generative AI models (e. , GANs, VAEs, transformers).

- Expertise in Retrieval-Augmented Generation (RAG) techniques, integrating retrieval mechanisms with generative models.

- Experience with LLaMA (Large Language Model Meta AI) models, including their development, fine-tuning, and deployment.

- Proficiency with vector databases (e. , Pinecone, Weaviate, FAISS) for efficient similarity search and retrieval tasks.

- Experience with big data technologies (e. , Hadoop, Spark) and data processing pipelines.

- Familiarity with cloud platforms (e. , AWS, Google Cloud, Azure).

- Expertise in deep learning techniques and neural networks.

- Proficiency in natural language processing (NLP) and computer vision, especially in generative AI and RAG applications.

- Experience with reinforcement learning and generative models.

- Familiarity with MLOps/DevOps practices and tools for ML (e. , Docker, Kubernetes).