Experience implementing ML-based systems, including model lifecycle management, monitoring, and MLOps pipeline setup.
Strong proficiency in Python (Pandas, Numpy, Jupyter, Scikit-Learn, XGBoost, Plotly).
Knowledge of SQL.
Experience with cloud platforms (AWS, GCP, Azure).
Requirements:
Develop and optimize ML models, ensuring scalability, monitoring, and integration with MLOps best practices.
Implement client requirements, from exploratory data analysis (EDA) to feature engineering and model lifecycle management.
Build ML Proof of Concepts (POCs) to validate and refine solutions.
Optimize models for performance, latency, memory, and throughput.
Job description
🚀 Join Our Data Products and Machine Learning Development Remote Startup! 🚀
Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.
We’re looking for a Machine Learning Engineer to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect! 🐶🚀
🚀 What We Do
Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.
🌟 Our Partnerships
Amazon Web Services
Astronomer
Databricks
🌟 Our Values
📊 We are Data Nerds
🤗 We are Open Team Players
🚀 We Take Ownership
🌟 We Have a Positive Mindset
🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀
Responsibilities 🤓
Develop and optimize ML models, ensuring scalability, monitoring, and integration with MLOps best practices.
Implement client requirements, from exploratory data analysis (EDA) to feature engineering and model lifecycle management.
Build ML Proof of Concepts (POCs) to validate and refine solutions.
Optimize models for performance, latency, memory, and throughput.
Apply statistical analysis techniques and develop regression models.
Design and maintain feature stores and data pipelines for ML workflows.
Research and implement emerging ML/AI techniques to enhance solutions.
Collaborate with stakeholders to align technical solutions with business needs.
Required Skills
Experience implementing ML-based systems, including model lifecycle management, monitoring, and MLOps pipeline setup.
Strong proficiency in Python (Pandas, Numpy, Jupyter, Scikit-Learn, XGBoost, Plotly).Knowledge of SQL
Experience with cloud platforms (AWS, GCP, Azure).
Nice to Have Skills 😉
Experience with ML workflows (Airflow, MLflow, H2OAI, Databricks, or similar).
Background in modern LLM technologies.
Understanding of Deep Learning frameworks (Keras, PyTorch, TensorFlow).Basic knowledge of Docker.
