OmniMD - Artificial Intelligence/Machine Learning Engineer - LLM Models (7-12 yrs)

About OmniMD.

OmniMD is a reputed healthcare IT vendor and has embarked on a journey into the Clinical Decision Support System (CDSS) space through its OmniScient suite of applications that are grounded in a workflow-integrated, user-friendly, standardized, interoperable and micro-servicesbased technology and knowledge-management ecosystem.

OmniScient has been as a qualified Clinical Decision Support Mechanism (qCDSM) for appropriate use criteria (AUC) under The Protecting Access to Medicare Act (PAMA) of 2014, contained within the Medicare Payment program of the Centers for Medicare & Medicaid Services (CMS), and enforced under 42 CFR 414.

94, to increase the rate of appropriate advanced diagnostic imaging services provided to Medicare beneficiaries.

OmniMD is expanding its framework to include Prior Authorization for commercial plans that require documentation of medical necessity and also the supporting electronic administrative workflow.

Job Overview :

We are seeking a highly skilled and experienced Senior Machine Learning/AI Engineer with expertise in Generative AI, Retrieval-Augmented Generation (RAG), LLaMA (Large Language Model Meta AI) models, and vectordatabases to join our dynamic team.

The ideal candidate will have a strong background in machine learning, deep learning, and generative AI, with experience in leveraging RAG techniques, LLaMA models, and vectordatabases to create groundbreaking solutions that address complex challenges across various industries.

You will be responsible for designing, developing, and deploying advanced machine learning models and algorithms, particularly in these areas, to solve complex problems and enhance our products and services.

Key Responsibilities :

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

DataAnalysis: Analyze and interpret complexdatasets 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 vectordatabase integration.

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

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

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

Mentorship: Provide guidance and mentorship to junior ML/AI engineers anddatascientists, particularly in the areas of generative AI, LLaMA models, RAG, and vectordatabases.

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

Qualifications :

Education : Bachelor's, master's, or Ph.in Computer Science,DataScience, Statistics, Mathematics, or a related field.

Experience :

- 2 to 15 years of experience in machine learning, AI, or a related field, with a minimum of 2to 5 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.g, TensorFlow, PyTorch, Keras).

- Extensive experience with generative AI models (e.g, 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 vectordatabases (e.g, Pinecone, Weaviate, FAISS) for efficient similarity search and retrieval tasks.

- Experience with bigdatatechnologies (e.g, Hadoop, Spark) anddataprocessing pipelines.

- Familiarity with cloud platforms (e.g, 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.g, Docker, Kubernetes).

- Strong problem-solving skills and the ability to think algorithmically.

- Experience withdatavisualization tools (e.g, Matplotlib, Seaborn).

- Proficiency in SQL and NoSQLdatabases.

- Experience with version control systems (e.g, Git).

- Communication Skills.

- Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

- Ability to work effectively in a collaborative team environment.

Preferred Qualifications :

- Experience in natural language processing (NLP) and computer vision.

- Publications or contributions to conferences/journals in the field of ML/AI, particularly in generative AI, LLaMA models, RAG, and vectordatabases.

- Experience with reinforcement learning and generative models.

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