Machine Learning Engineer

October 31

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Logo of Mechanized AI

Mechanized AI

Machine Learning • MLOps • Data Engineering • Data Pipeline • Data Cleaning

11 - 50

Description

• Contribute to building and enhancing our Mechanized AI platform and AI-enabled products including mAI • Modernize • Serve as ML SME on client projects as needed • Design ML systems • Research and implement appropriate ML algorithms and tools • Select appropriate datasets and data representation methods • Run ML tests and experiments • Perform statistical analysis and fine-tuning using test results • Train and retrain systems when necessary • Extend existing ML libraries and frameworks • Stay current with emerging technologies and ML best practices to continuously improve our methodologies and tools

Requirements

• 4+ years of ML experience at a start-up or larger enterprise – high priority • 6+ months of experience with Large Language Models (LLMs) and Generative AI (GenAI) applications – high priority • Client delivery experience – high priority • Effective written and oral communications skills (C1/C2 - advanced/proficient level English is required) – high priority • Bachelor’s degree in computer science, software engineering or related field • Experience with cloud environments (e.g., AWS, Azure, GCP) • Experience with ML frameworks and libraries (TensorFlow, PyTorch, Keras, scikit-learn) • Experience developing, deploying, and managing/monitoring models • Knowledge of containerization technologies (e.g., Docker, Kubernetes) and microservices architecture • Expertise in Object-Oriented Programming (OOP) principles and unit test-driven development methodologies • Advanced experience in NLP techniques and applications • Strong proficiency in Python programming • Familiarity with prompt engineering approaches and best practices • Knowledge of data structures, data modeling, and software architecture • Strong analytical and problem-solving skills, with ability to propose innovative solutions and troubleshoot issues • Ability to work independently and as part of a collaborative team in a fast-paced environment

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