Machine Learning Engineer (Contractor)

Tech HoldingRemote - Mexico, Remote1w ago

Remote Senior LATAM Cloud Computing Artificial Intelligence Machine Learning Engineer JAX Python Learning & Development GCP AWS

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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. • Strong understanding of data structures, algorithms, and software engineering principles. • Experience with large-scale data processing frameworks (Spark, Dask, or similar). • Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field (or equivalent experience). • 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. • Exposure to modern techniques such as transformer architectures, embeddings, or large language models. • Experience working in a fast-paced startup or product-driven environment.

Responsibilities

• 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. • Research and implement modern machine learning techniques, including deep learning and large language models where appropriate. • Work closely with product and cross-functional teams to translate business requirements into technical solutions. • Deploy and maintain machine learning models in production environments. • Monitor model performance, conduct experiments and A/B testing, and continuously improve model accuracy and reliability. • Contribute to the team's engineering best practices, including code reviews, documentation, and knowledge sharing.