Senior Software Engineer - ML Ops

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Requirements:

• 5-7 Years of Software engineering experience
• Experience with cloud computing environments, especially AWS and container-based deployment using Docker and Kubernetes
• Experience working with python 2-3 years minimum
• Experience operating services in production environments
• A strong understanding of software design principles, software architecture and design patterns as well as software development best practices, including testing, version control, and continuous integration
• Experience with infrastructure as code tools like Terraform or AWS CDK
• Experience in monitoring and performance of Production platforms using tech stacks and tools such as Datadog, ELK, Grafana, Prometheus

Responsibilities:

• Design, develop, test, and maintain our complex software applications, ensuring high-quality code and adherence to best practices.
• Provide technical leadership and guidance to junior engineers and the development team, including code reviews, architecture decisions, and mentoring.
• Contribute to the design and architecture of software systems, ensuring scalability, maintainability, and performance
• Analyze and solve complex technical problems, and make recommendations for improvements and optimizations.
• Implement and advocate for best practices in testing and quality assurance, including unit testing, integration testing, and automated testing.
• Participate in code reviews and provide constructive feedback to ensure code quality and consistency.
• Stay current with emerging technologies, tools, and programming languages and apply them where relevant to improve software development processes.
• Ensure software adheres to security standards and compliance requirements, addressing vulnerabilities and potential risks.
• Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)
• Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools
• Data science model review: run code and refactor, optimize, containerize, deploy, version, and monitor its quality
• Validate and add automated tests for Data Science models
• Work closely with a team of researchers and data scientists to productionize and document research innovations