Machine Learning Engineer

Syren Cloud Inc


Date: 1 week ago
City: Remote, Remote
Contract type: Full time
Remote
We are seeking a skilled and motivated Machine Learning Engineer to join our dynamic team. In this role, you will design, develop, and deploy machine learning models and scalable AI systems to solve real-world problems. You will work closely with data scientists, software engineers, and business stakeholders to turn data into actionable insights and deliver innovative solutions that drive business impact.

Key Responsibilities

  • Model Development:
  • a. Design, build, train, and optimize machine learning models using state-of-the-art techniques.
  • b. Conduct exploratory data analysis to identify patterns and inform feature engineering.
  • Data Pipeline Management:
  • a. Collaborate with data engineers to build robust, scalable, and efficient data pipelines.
  • b. Ensure data quality, preprocessing, and cleaning for ML model input.
  • Deployment & Monitoring:
  • a. Deploy ML models to production environments using MLOps practices.
  • b. Monitor and evaluate the performance of models in production, iterating to improve accuracy and efficiency.
  • Collaboration & Integration:
  • a. Work with cross-functional teams to integrate ML models into existing products and services.
  • b. Translate business problems into machine learning solutions.
  • Research & Innovation:
  • a. Stay updated with the latest advancements in machine learning and AI.
  • b. Experiment with new tools, algorithms, and frameworks to continuously improve processes.
  • Documentation & Communication:
  • a. Document model development, data processes, and experiments for reproducibility.
  • b. Communicate findings and technical details to both technical and non-technical stakeholders.

Qualifications

Must-Have:

  • Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field.
  • Proven experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Proficiency in programming languages such as Python, Java, or Scala.
  • Strong understanding of algorithms, statistics, and applied mathematics.
  • Experience with cloud platforms (AWS, Azure, GCP) for ML model deployment.

Nice-to-Have

  • Familiarity with MLOps tools like MLflow, Kubeflow, or Airflow.
  • Knowledge of big data tools like Apache Spark or Hadoop.
  • Understanding of deep learning, NLP, or computer vision.
  • Experience with containerization and orchestration tools like Docker and Kubernetes.

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