About the Role
The MLOps Engineer will support the design, development, testing, deployment, and maintenance of software systems and AI/ML-enabled solutions. The role involves working in cloud-native and microservices environments, developing APIs, handling telemetry data, and supporting ML/AI workloads across structured and unstructured datasets.
Responsibilities
- Support the design and development of software solutions and business functionality.
- Contribute to writing, testing, debugging, and maintaining code.
- Support automated testing and software deployment activities.
- Collaborate with product teams to deliver software components.
- Integrate and build solutions using automation, coding, and third-party software.
- Assist in building and debugging large-scale distributed systems.
- Develop functionality in microservices environments using APIs and telemetry data.
- Support execution of ML/AI algorithms on structured and unstructured data.
- Maintain technical documentation, user documentation, and operational procedures.
- Assist with code refactoring and code reviews.
Requirements
- Bachelor's degree or equivalent in Computer Science, Engineering, or a related field.
- Knowledge of cloud platforms including AWS, Google Cloud Platform, Microsoft Azure, and Microsoft 365.
- Proficiency in one or more programming languages such as Python, Java, JavaScript, Node.js, C++, or C#.
- Understanding of data structures, algorithms, and software design principles.
- Knowledge of microservices architecture and API development.
- Experience with SQL and NoSQL databases including Elasticsearch, MongoDB, and Cassandra.
- Knowledge of container technologies such as Kubernetes, Docker, and LXC/LXD.
- Familiarity with Agile, Lean, and Test-Driven Development practices.
- Understanding of CI/CD concepts and cloud-based deployments.
- Strong analytical, debugging, collaboration, and communication skills.
Preferred Qualifications
- Microsoft Certified Azure Fundamentals certification.
- Agile certifications.
- Experience working with geo-distributed teams.
- Experience with the complete software delivery lifecycle including source control, testing, CI, defect management, and work tracking tools.
- Experience with Agile, Lean, Continuous Delivery, DevOps, and analytics-driven processes.
- Familiarity with large datasets and applying ML/AI algorithms.
- Experience developing microservices and RESTful APIs.
