MLOps Engineer: Required Qualifications & Experience: Masters +5 years of experience or Bachelor's Degree + 7 years of experience Understanding ML development lifecycle (labeling, training, testing, deployment, monitoring) Experience deploying high throughput ML models to a production environment. In particular, managing and deploying custom built applications (e.g. with limited to no use of Docker containers) Expert in Python and dev/test/prod deployment pipelines Requires proficiency in: Java Query languages such as SQL (PostgreSQL) and NoSQL (MongoDB) Other programming concepts and tools such as regular expressions and version control (Git) Experience managing ML model performance, such as memory management and debugging Experience with ML frameworks: Tensorflow, Pytorch, ONNX, Scikit-Learn, Pandas, Keras, Tesseract, Sagemaker, AWS++6 Understanding of ethical AI principles Excellent oral and written communication skills, and time management skills Formulate and rapidly prototype various approaches as well as effectively communicate the pros and cons of each. Ability to contribute to a high-performing, motivated workgroup by applying interpersonal and collaboration skills to achieve project goals Provide technical guidance in the fields of NLP, Machine Learning, Statistical Methods Provide data-driven approaches to tackle various business and NLP problems Experience with statistical model building (particularly classification) Ability to leverage domain knowledge as well as methods to improve model performance The following skills are not required but are highly desired: Experience with NLP and ML technologies and concepts such as: BERT, CNN, RNN, SVMs, k-Nearest Neighbors, Linear/Logistic Regression and Classification, Ensemble Methods, Graphical Models, Clustering, MLPs, Transformers, N-gram, Skipgram Knowledge of and experience using various NLP approaches, particularly: NLP preprocessing steps such as tokenization and vectorization Pattern recognition/feature extraction (N-Gram, Skipgram, etc.) Supervised, Unsupervised, and Semi-Supervised learning techniques Practical experience leveraging open source libraries for emerging approaches to NLP Chunking/Tokenization Semantic parsing Information extraction Experience building, deploying, and maintaining infrastructure and pipelines for LLMs. Experience integrating prompt management into pipelines. Understanding of challenges such as token usage, cost, hallucination rates.