Das ist der Job
Focus on delivering reliable and high-performing ML solutions.
Darum lohnt es sich
Research and implement modern machine learning techniques, including deep learning and large language models where appropriate. Exposure to modern techniques such as transformer architectures, embeddings, or large language models. xayajpt Experience working in a fast-paced startup or product-driven environment.
Culture & Benefits Remote Work Opportunity Machine Learning Engineer (AI): Design, build, and deploy machine learning models that power data-driven products and insights across the organization with an accent on machine learning, data engineering, and scalable systems.
Bewerber sollten sich die Zeit nehmen, alle Elemente dieser Stellenanzeige sorgfältig zu lesen Bitte bewerben Sie sich umgehend. Location: Remote (USA) Company Tech Holding is a full-service consulting firm delivering predictable outcomes and high-quality solutions.
What you will do Design, develop, and deploy machine learning models to solve business problems across large-scale datasets. Build and optimize machine learning pipelines for data preparation, model training, and inference. Collaborate with data engineers and software engineers to develop scalable ML infrastructure and pipelines.
Deploy and maintain machine learning models in production environments. Requirements 5+ years of professional experience in machine learning engineering or a related role. Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
Experience building, training, and deploying machine learning models in production environments. Experience working with data pipelines and large-scale datasets. Proficiency with cloud platforms (AWS, GCP, or Azure) and familiarity with MLOps practices.
Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field (or equivalent experience). Nice to have Experience working with natural language processing, computer vision, recommendation systems, or other applied ML domains. Familiarity with model deployment, experiment tracking, and model monitoring tools.
Experience working with distributed systems and scalable ML infrastructure.
