Machine Learning Engineer

As a Machine Learning Engineer at (Netbounce Global), you will work on designing, developing, and deploying machine learning models and systems to solve complex real-world problems. You will collaborate closely with data scientists, software engineers, and product managers to build robust, scalable, and efficient ML solutions. Key Responsibilities Develop and implement machine learning models and algorithms for various applications, such as predictive analytics, natural language processing, and computer vision.

Analyze large datasets to derive actionable insights and build data-driven solutions. Collaborate with cross-functional teams to integrate machine learning models into production environments. Optimize machine learning algorithms for speed, accuracy, and scalability.

Conduct performance evaluations and fine‑tuning of models to ensure they meet business objectives. Keep up to date with the latest developments in machine learning, AI, and related technologies, and integrate these innovations into the team's workflow. Provide mentorship and guidance to junior engineers and team members.

Write clean, maintainable, and efficient code while following industry best practices. Required Qualifications Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field. Proven experience (0-4 years) working as a Machine Learning Engineer or in a similar role.

Strong proficiency in programming languages such as Python, R, or Java. Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit‑learn, or Keras. Solid understanding of machine learning concepts including supervised/unsupervised learning, deep learning, and reinforcement learning.

Strong knowledge of algorithms, data structures, and software engineering best practices. Experience with data wrangling, preprocessing, and feature engineering. Familiarity with cloud computing platforms (AWS, GCP, Azure) for ML model deployment.

Knowledge of SQL and experience working with large datasets. Experience with version control systems (e.g., Git). Preferred Qualifications Masters in Computer Science, Artificial Intelligence, or a related field.

Experience with deploying machine learning models in a production environment. Knowledge of containerization technologies such as Docker and Kubernetes. Familiarity with big data technologies such as Hadoop, Spark, or Kafka.

Strong analytical, problem‑solving, and communication skills. Competitive salary and performance‑based bonuses. Health, dental, and vision insurance. 401(k) with company matching.

Generous paid time off and holiday schedule. Opportunities for professional development and continuing education. Collaborative and dynamic work environment. #J-18808-Ljbffr