Machine Learning Engineer

Key Skills

Analytical and Problem solving, Argo, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, DevOps, Docker, Effective communication skills, Git, Infrastructure as code, Kubernetes-K8s, Machine learning techniques, MLflow, NumPy, Optimization, Pandas, Python Programming, REST API, Scikit-learn, SQL

Job Description

Role Overview: As a Machine Learning Engineer specializing in DevOps and ML, you will play a pivotal role in designing, implementing, and optimizing end-to-end machine learning pipelines and infrastructure. You will collaborate closely with data scientists, software engineers, and other cross-functional teams to deploy, monitor, and maintain machine learning models that drive actionable insights and improve business outcomes. Additionally, you will be responsible for building scalable REST APIs to serve machine learning models and data to various applications and ensuring the ongoing maintenance of these systems.

Responsibilities:

• Design, build, and maintain scalable and efficient machine learning infrastructure and pipelines for model development, training, deployment, and ongoing maintenance.
• Collaborate with data scientists to integrate machine learning models into production systems and applications.
• Implement best practices for version control (Git), code integration, and automated testing within the machine learning workflow.
• Develop and maintain CI/CD pipelines for continuous integration and deployment of machine learning models to scalable environments including batch and streaming inference applications
• Ensure high availability, reliability, and performance of machine learning systems in a cloud environment, particularly Azure.
• Utilize Docker and Kubernetes to containerize and orchestrate machine learning applications and services.
• Design, develop, and deploy scalable REST APIs to serve machine learning models and data to applications.
• Build and optimize scalable machine learning pipelines to process and transform data for model training and inference.
• Monitor and troubleshoot deployed machine learning systems, promptly addressing any issues that arise and performing necessary maintenance.
• Stay current with the latest advancements in DevOps, machine learning, and cloud technologies, and assess their potential applicability to our projects.
• Collaborate with cross-functional teams to define technical requirements, perform code reviews, and contribute to the overall technical direction of the company.

Qualifications:

• Bachelor's or higher degree in Computer Science, Engineering, or a related field.
• Proven experience (4+ years) as a Machine Learning Engineer, Data Engineer, or Software Engineer with a heavy focus on DevOps and ML infrastructure.
• Strong proficiency in Linux systems and administration.
• Expertise in Python programming and familiarity with relevant DS/ML libraries (e.g. MLFlow, pandas, numpy, scikit-learn, kedro).
• Proficiency in SQL and database administration fundamentals
• Experience with building and deploying scalable web applications including understanding client-server architecture, REST APIs, and managing securing certificates
• Experience designing and optimizing scalable machine learning pipelines.
• Experience deploying applications with containerization technologies such as Docker and orchestration tools like Kubernetes.
• In-depth knowledge of cloud platforms, particularly Azure, and experience deploying machine learning workloads on cloud infrastructure.
• Expert in DevOps principles, CI/CD practices & tools (e.g. ArgoCD), and version control through Git
• Excellent problem-solving skills and the ability to troubleshoot complex technical issues efficiently.
• Strong communication and collaboration skills to work effectively within cross-functional teams.
• Prior exposure to the insurance or financial industry is a plus