Senior ML Engineer

ML EngineerEXPERIENCE: 5 – 9 YEARS Position SummaryAs an ML Engineer (MLOps), you will take machine-learning models and AI pipelines from proof-of-concept through to scalable, reliable production deployment. You will own deployment, monitoring, and optimisation across both edge and cloud environments. ResponsibilitiesDeployment: Deploy ML models and AI pipelines from PoC / development to production, ensuring they scale efficiently and maintain high performance through seamless CI/CD integration and orchestration.Monitoring & Maintenance: Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.Model Optimisation & Pruning: Optimise models for inference speed and resource efficiency using techniques such as quantisation, pruning, and knowledge distillation for edge and cloud deployment.Data Preprocessing: Perform data collection, cleaning, and feature engineering to prepare datasets for training.Model Training & Tuning: Implement continuous / semi-continuous training and evaluation workflows to maintain accuracy over time, and fine-tune models for optimal performance.Collaboration: Work with data scientists, software engineers, DevOps, and product managers to understand requirements and deliver ML solutions.Documentation: Maintain clear, organised documentation of code, models, and processes. QualificationsBachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI, or a related field.5 – 9 years of relevant experience.Proficiency in Python and libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.Knowledge of model deployment, containerisation, and orchestration (Docker, Kubernetes).Knowledge of SQL and NoSQL databases.Familiarity with one or more cloud platforms (AWS, GCP, or Azure).Familiarity with MLOps tools such as MLflow, ClearML, Azure ML, or AWS SageMaker.Strong understanding of deep learning, reinforcement learning, and other ML techniques.Preferred Qualifications·      Experience deploying computer-vision models to edge devices or low-resource environments.·      Familiarity with infrastructure-as-code tools and observability platforms.·      Contributions to open-source computer-vision projects or relevant publications. Core Technical Skills·      Languages: Python.·      Frameworks & Libraries: PyTorch, TensorFlow, OpenCV, Scikit-learn, Pandas, NumPy, FastAPI.·      Serving & Deployment: Docker, Kubernetes, GitLab CI (CI/CD).·      Databases: PostgreSQL, MySQL, MongoDB, Elasticsearch, Neo4j.·      Deep-Learning Architectures: CNN, LSTM, GAN, Transformers, LLM.·      MLOps & Distributed Computing: MLflow, Kubeflow, Ray, ClearML.·      Message Brokers & GPU: RabbitMQ, Kafka; CUDA, RAPIDS, Numba.·      Cloud Platforms: AWS, Azure, GCP.