Machine Learning Engineer

We're Hiring:
Mid-Level Machine Learning Engineer
We're looking for a Mid-Level Machine Learning Engineer to join our team. If you're passionate about building intelligent systems that solve real-world problems at scale, we'd love to hear from you.

About the Role
You'll design, develop, and deploy machine learning models and pipelines that power core product features. You'll work closely with data scientists, software engineers, and product teams to bring ML solutions from research to production.

What You'll Do

• Design, train, and optimize machine learning models for classification, regression, NLP, computer vision, or recommendation systems

• Build and maintain end-to-end ML pipelines from data ingestion to model serving

• Collaborate with data engineers to ensure clean, reliable, and scalable data pipelines

• Deploy and monitor models in production environments, ensuring performance and reliability

• Conduct experiments, A/B tests, and performance evaluations to improve model accuracy

• Write clean, well-documented, and reproducible code for training and inference workflows

• Stay current with the latest ML research and evaluate new techniques for practical application

• Partner with product and engineering teams to define ML-driven features and solutions

• Contribute to MLOps practices including model versioning, monitoring, and automated retraining

What We're Looking For

• 3-5 years of professional experience in machine learning or applied AI

• Strong proficiency in Python and ML frameworks such as Tensor Flow, PyTorch, or scikit-learn

• Experience building and deploying ML models in production environments

• Solid understanding of core ML concepts including supervised and unsupervised learning, feature engineering, model evaluation, and hyperparameter tuning

• Proficiency with SQL and experience working with large-scale datasets

• Familiarity with cloud platforms (AWS, GCP, or Azure) and their ML services (Sage Maker, Vertex AI, Azure ML)

• Strong foundation in mathematics, statistics, and linear algebra

• Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or a related field, or equivalent practical experience

Nice to Have

• Experience with MLOps tools such as MLflow, Kubeflow, or Airflow

• Familiarity with containerization and orchestration (Docker, Kubernetes)

• Experience with large language models, transformers, or generative AI

• Knowledge of distributed computing frameworks such as Spark or Ray

• Published research or contributions to open-source ML projects