We are looking for a Mid-Level Machine Learning Engineer to design, develop, and deploy machine learning solutions across the organization. In this role, you will work on the full ML lifecycle — from model experimentation to production deployment and monitoring. You will collaborate closely with data scientists, product managers, and engineers to build robust, scalable systems that drive measurable business value.
This role is ideal for engineers who are confident in implementing ML pipelines and are ready to take ownership of critical components in real-world applications.
Key Responsibilities
Design and develop machine learning models using structured and unstructured data.
Build and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring.
Collaborate with cross-functional teams to translate business problems into ML solutions.
Optimize model performance (accuracy, latency, scalability) for production environments.
Integrate models with APIs or applications using RESTful services or model serving tools.
Monitor deployed models for drift, performance degradation, or failures, and implement retraining strategies.
Contribute to documentation, code quality standards, and technical reviews.
Education:
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
Experience:
2–4 years of hands-on experience in building and deploying machine learning models in a production setting.
Proficient in Python and ML frameworks like Scikit-learn, TensorFlow, or PyTorch.
Strong understanding of machine learning concepts, model evaluation, and hyperparameter tuning.
Experience with data preprocessing, feature selection, and pipeline automation (e.g., with Sklearn Pipelines, Airflow).
Solid SQL skills and experience working with relational or NoSQL databases.
Familiarity with model deployment tools and techniques (e.g., Flask, FastAPI, Docker, Kubernetes).
Experience with cloud ML services (e.g., AWS SageMaker, GCP Vertex AI, Azure ML).
Version control using Git, and familiarity with CI/CD practices for ML workflows.
Ability to work independently and manage multiple priorities with minimal supervision.
Experience with MLOps tools like MLflow, DVC, or Kubeflow.
Familiarity with big data tools (e.g., Spark, Hadoop) and data streaming platforms (Kafka).
Exposure to experimentation and A/B testing frameworks.
Contribution to open-source ML projects or publications is a plus.