Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer with deep expertise in MLOps to lead the design, development, and deployment of scalable machine learning models and infrastructure. In this role, you will collaborate with data scientists, data engineers, software engineers, and Dev Ops teams to build, maintain, and optimise the entire lifecycle of machine learning systems from pilot to production.

• Design, develop, and deploy robust machine learning models for various use cases such as classification, regression, and deep learning models.
• Lead the full ML lifecycle, from model training and experimentation to production deployment and post-deployment monitoring.

• Design and implement scalable MLOps pipelines for automating the end-to-end machine learning lifecycle (data preparation, model training, testing, deployment, monitoring, and versioning).
• Collaborate with Dev Ops to build and manage CI/CD pipelines for machine learning models and APIs.
• Optimise model performance and ensure scalability of model-serving architecture (e. g., Kubernetes, Docker, AWS/GCP/Azure).
• Automate data pipelines and ensure smooth orchestration of workflows using tools like Airflow, Kubeflow, or MLFlow.

• Work closely with data scientists and engineering teams to integrate ML models into production systems.
• Lead and mentor junior engineers on best practices in machine learning, software engineering, and MLOps.
• Act as a bridge between data science and engineering teams to ensure the seamless operation of machine learning systems.

• Implement and maintain tools for monitoring, alerting, and managing ML model performance in production (e. g., Prometheus, Grafana).
• Continuously improve model reliability, accuracy, and efficiency through rigorous testing, validation, and retraining.

• Ensure models adhere to security best practices and comply with industry standards for data privacy and regulatory requirements.
• Establish proper version control for models and data to support compliance and auditing needs.

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field.
• 5+ years of hands-on experience in designing, developing, and deploying machine learning systems.
• 3+ years of experience in MLOps practices, including building and managing pipelines for continuous integration and continuous delivery (CI/CD).
• Proven track record of delivering high-quality machine learning products into production at scale.

• Deep understanding of ML algorithms, statistical modelling, and deep learning frameworks (e. g., Tensor Flow, PyTorch, Scikit-Learn).
• Experience with MLOps frameworks and tools like MLflow, Kubeflow, Airflow, Seldon, and Tecton.
• Experience in Dev Ops tools and practices, including CI/CD, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform).
• Strong experience with cloud services (AWS, GCP, or Azure), specifically around ML services like Sage Maker, Vertex AI, or Azure ML.
• Proficiency in Python and familiarity with other languages such as Java, Scala, or R.
• Experience with data pipelines, ETL/ELT processes, and databases (SQL and No
  
  SQL).

• Excellent problem-solving skills with a focus on delivering high-impact solutions.
• Strong communication skills, with the ability to work effectively in cross-functional teams.
• Demonstrated leadership and mentorship abilities.