Senior Machine Learning Engineer

About the job Senior Machine Learning Engineer Onsite (Austin, Tx) only locals Experience: 10 + years Client Name: Confidential for now **No Visa Sponsorship** Key Responsibilities Help fine-tune or prompt engineer large language models (LLMs) for various healthcare applications across various customer engagements. Develop and refine our approach to handling vision-based data using state-of-the-art VLM based models capable of processing and analyzing medical documents, healthcare forms in various formats and other visual data accurately. Create and enhance classic NLP models to understand and generate human language in healthcare settings, supporting clinical documentation, and patient interaction. Collaborate with multi-disciplinary teams including data scientists, ML engineers, healthcare clients, and product managers to deliver robust solutions. Ensure models are efficiently deployed and integrated into healthcare systems, maintaining high performance and scalability. Mentor and provide guidance to junior engineers and data scientists, fostering a culture of continuous learning and innovation. Conduct rigorous testing, validation, and tuning of models to ensure accuracy, reliability, and compliance with healthcare standards. Deep understanding of various training techniques including distributed training on GPUs and TPUs. Stay informed on the latest research, tools, and technologies in machine learning, particularly those applicable to language and vision processing in healthcare. Document methodologies, model architectures, and project outcomes effectively for both technical and non-technical audiences. Qualifications Bachelor's or master's degree in computer science, Engineering, Data Science, or a related field. 5-7 years of experience in machine learning engineering, with a significant track record in the developing production grade models and model pipelines in a regulated industry such as healthcare. Hands-on expertise in working with large language models (e.g., GPT, BERT), computer vision models, and classic NLP technologies. Proficient in programming languages such as Python, with extensive experience in ML libraries/frameworks like TensorFlow, PyTorch, OpenCV, etc. Strong understanding of deep learning techniques, model fine-tuning, hyper parameter optimization, and model optimization. Proven experience in deploying and managing ML models in production environments. Excellent analytical skills, with a problem-solving mindset and the ability to think strategically. Strong communication skills for articulating complex concepts to diverse audiences. Working knowledge or experience in MLOps and LLMOps using tools like MLflow, Kubeflow. Working knowledge of basic software engineering principles and best practices. Demonstrated working knowledge and experience on classic ML techniques and frameworks. Nice to have: Knowledge of cloud vendor-based ML platforms such as Azure ML, SageMaker Nice to have competencies Large/Complex organization experience in deploying NLP/ML in production. Experience in efficiently scaling ML model training and inferencing. Experience with Big Data technologies using Kafka, Spark, Hadoop, Snowflake