Job Summary:
As a Senior Machine Learning Operations (MLOps) Engineer you will be instrumental in deploying robust scalable machine learning solutions. You will ensure these are tailored to meet the expansive needs of a client in healthcare services. This role demands a high level of proficiency in machine learning technologies and programming coupled with rigorous vetting processes to maintain the highest standards of data integrity and security.
Key Responsibilities:
- Rapidly develop and deploy productionready ML models with a focus on scalability and monitoring across a broad range of applications within healthcare.
- Write efficient maintainable and scalable Python code tailored to our specific business needs.
- Build highperformance multitenant deployment architectures and sophisticated model monitoring systems.
- Directly engage with internal stakeholders to incorporate feedback and refine our MLdriven products through quick iteration cycles.
- Uphold stringent security protocols and processes in the deployment and maintenance of machine learning models.
- Drive the continuous advancement of MLOps practices within the healthcare industry by developing innovative solutions and advocating for best practices.
- Requirements:
- Minimum 3 years of experience with transformerbased models and NLP preferably in a healthcare context.
- Strong track record of finetuning running largescale training jobs and managing model servers like vLLM TGI or TorchServe.
- Proficiency in data science tools such as Pandas Notebooks Numpy Scipy.
- Experience with both relational and nonrelational databases.
- Extensive experience with TensorFlow or PyTorch and familiarity with HuggingFace.
- Knowledge of model analysis and experimentation frameworks such as MLFlow W&B
- and tfma is preferred.
- Comfortable with a Linux environment and stringent data security practices.
- Must pass a rigorous vetting process including extensive background checks to
- ensure the highest standards of data security and integrity.
pytorch,linux environment,nlp,python,healthcare,mlops,data security,machine learning,data science tools,scalability,huggingface,learning,transformer-based models,monitoring,operations,tensorflow