ML Engineer (MLOps)

We are looking for an experienced ML Engineer (MLOps) to take machine learning models and AI pipelines from proof of concept to scalable, reliable production deployment.In this hands-on role, you will be responsible for deploying, monitoring, and optimizing machine learning solutions across cloud and edge environments. You will work closely with Data Scientists, Software Engineers, DevOps teams, and Product Managers to deliver robust, efficient, and production-ready AI solutions.Key ResponsibilitiesModel Deployment: Deploy machine learning models and AI pipelines from development and proof of concept into production, ensuring scalability, reliability, and performance through CI/CD integration and orchestration.Monitoring and Maintenance: Implement monitoring, evaluation, and maintenance strategies to maintain model accuracy, reliability, and performance in production.Model Optimization: Optimize models for inference speed and resource efficiency using techniques such as quantization, pruning, and knowledge distillation, particularly for edge and cloud deployment.Data Preparation: Perform data collection, cleaning, preprocessing, and feature engineering to prepare high-quality datasets for model training.Model Training and Tuning: Develop continuous or semi-continuous training and evaluation workflows, fine-tune models, and maintain model performance over time.MLOps and Automation: Contribute to automated ML pipelines, model lifecycle management, and reproducible workflows using appropriate MLOps tools.Cross-functional Collaboration: Work with Data Scientists, Software Engineers, DevOps teams, and Product Managers to translate business and technical requirements into reliable ML solutions.Documentation: Maintain clear documentation of code, models, deployment workflows, and operational processes.Required Qualifications and ExperienceBachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, or a related field.5–9 years of relevant professional experience in machine learning engineering or related roles.Strong proficiency in Python and experience with ML libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.Practical knowledge of machine learning model deployment, containerization, and orchestration using Docker and Kubernetes.Experience with SQL and NoSQL databases.Familiarity with at least one cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP).Experience with MLOps tools such as MLflow, ClearML, Azure Machine Learning, or AWS SageMaker.Strong understanding of deep learning, reinforcement learning, and other machine learning techniques.Preferred QualificationsExperience deploying computer vision models to edge devices or resource-constrained environments.Familiarity with infrastructure-as-code tools and observability platforms.Experience with model optimization, GPU acceleration, or distributed computing.Contributions to open-source computer vision projects or relevant technical publications.Technical EnvironmentProgramming and ML: Python, PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, OpenCV, FastAPI.Deployment and CI/CD: Docker, Kubernetes, GitLab CI/CD.MLOps and Distributed Computing: MLflow, Kubeflow, Ray, ClearML.Cloud: AWS, Azure, GCP.Databases: PostgreSQL, MySQL, MongoDB, Elasticsearch, Neo4j.Deep Learning: CNNs, LSTMs, GANs, Transformers, and LLMs.Messaging and GPU Computing: RabbitMQ, Kafka, CUDA, RAPIDS, Numba.If you have strong hands-on experience deploying and operating machine learning models in production, we would love to hear from you.