Machine Learning Engineer

Spring Communications

Responsibilities:
Design, develop, and deploy machine learning models and algorithms to solve business challenges.
Work closely with data scientists, software engineers, and business stakeholders to define project requirements and objectives.
Preprocess, clean, and analyze large datasets to prepare them for model training.
Optimize machine learning models for scalability, accuracy, and performance in production environments.
Build and maintain the infrastructure for data pipelines and model training at scale.
Evaluate and select appropriate machine learning techniques, tools, and frameworks.
Perform A/B testing, model evaluation, and hyperparameter tuning to ensure robust outcomes.
Collaborate with cross-functional teams to integrate machine learning solutions into existing systems.
Stay up-to-date with the latest advancements in artificial intelligence and machine learning.
Document processes, model architectures, and best practices for team knowledge sharing.

Requirements:
Proficiency in programming languages such as Python, R, Java, or C++.
Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization (e.g., Docker, Kubernetes).
Knowledge of data engineering concepts, including ETL pipelines and databases (SQL/NoSQL).
Solid understanding of statistical analysis, probability, and mathematical optimization techniques.
Familiarity with version control systems (e.g., Git) and CI/CD pipelines.
Excellent problem-solving, analytical, and communication skills.
Ability to work independently and collaboratively in a fast-paced environment.
Preferred Skills:
Experience with Natural Language Processing (NLP), computer vision, or deep learning.
Knowledge of big data technologies like Apache Spark, Hadoop, or similar.
Understanding of MLOps practices and model lifecycle management.
Familiarity with edge computing and deploying models on embedded devices.