Healthtech, digital solutioning & more

Reaching the level of an AI/ML Architect specializing in Generative AI typically requires a combination of strong technical expertise, continuous learning, and career progression through increasingly complex roles in AI and data science. Below are some common career routes and realistic examples of professionals progressing to this level : Career Routes 1. Academic to Industry Path Phase 1: Education and Research Earn a Bachelor's/Master's in Computer Science, Mathematics, or a related field. Pursue a Ph.D. in AI, ML, or NLP with a focus on generative AI topics such as transformers, GANs, or large language models. Publish research papers or contribute to open-source AI projects. Phase 2: Entry into Industry Begin as a Research Scientist or AI Engineer in a research-driven organization or lab. Phase 3: Senior Roles Transition to senior roles like Lead ML Engineer or Principal Data Scie...

JD of: AI/ML Architect with expertise in Generative AI to develop and deploy advanced machine learning models that solve real-world business challenges. The ideal candidate should have a strong understanding of transformer architectures, a hands-on approach to handling large datasets, and the ability to scale solutions in production environments. -----  Responsibilities Model Development: Build and fine-tune generative AI models such as GPT, BERT, T5, GANs, VAEs, and autoregressive models. Experiment with multimodal AI models combining text, images, and other data modalities. Data Handling and Preprocessing: Collect, clean, preprocess, and transform large and complex datasets for model training. Perform tokenization, text normalization, and feature extraction for unstructured data. Automate data preprocessing pipelines using Python scripts or ETL frameworks. Model Optimization and Deployment: Implement techniques like transfer learning, fine-tuning, and hyperparameter optimization ...

 Used this prompt to get my work done: Input ( copied data from a PPT and pasted after the prompt): based on frequency percentage , divide number 5 into each item.:   Incorrect DOS split –phase 1-PH​ In progress​ 5%​ Incorrect DOS split –phase 2-PH​ In backlog​ 6%​ Incorrect code prediction -AI MODEL stage-PH​ In backlog​ 4%​ Inaccuracies in valid & invalid, incorrect document identification-PH​ In backlog​ 5%​ Incomplete/incorrect logic applied for MEAT/TAMPER-PH​ In backlog​ 4%​ Issue AI output/AI MODEL prediction-PH​ In backlog​ 3%​ Relevant DOS logic-PH​ In backlog​ 5%​ Section split issue-PH​ In backlog​ 3%​ Output: To distribute the number 5 based on the given percentages, we can calculate the share for each category by multiplying 5 by each percentage and then rounding to an appropriate precision. Here’s the calculation: Categories and their percentages Incorrect DOS split – Phase 1-PH : 5% Incorrect DOS split – Phase 2-PH : 6% Incorrect code prediction - AI MO...

  In medical coding, these terms represent different types of codes used to classify and describe healthcare services, diagnoses, and procedures: 1.      ICD (International Classification of Diseases): A diagnostic coding system used to identify diseases, conditions, and other health-related problems. For example, ICD-10 is the current version used in many countries for medical diagnosis coding. 2.      CPT (Current Procedural Terminology): Codes created by the American Medical Association to document medical, surgical, and diagnostic services. CPT codes describe what was done to the patient during the encounter. 3.      E/M (Evaluation and Management): A subset of CPT codes specifically for coding physician-patient encounters, including office visits, hospital visits, and consultations. E/M codes describe the type, complexity, and time spent on patient interactions. 4.      Modifier : Codes...