The job is looking to solve the problem of bridging data science experiments with production systems by building robust, scalable AI-driven software solutions and ensuring seamless integration between data science and production environments.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
- Minimum of 5 years of experience in software development, with a strong emphasis on Python programming.
- Proficient in Python web frameworks such as Django, Flask, or FastAPI.
- Solid understanding of object-oriented programming principles, design patterns, and software architecture.
- Experience with relational databases and ORM frameworks like SQLAlchemy.
- Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes.
- Knowledge of cloud platforms (e.g., AWS, Azure, or GCP) and their services.
Responsibilities
- Collaborate with cross-functional teams to understand business requirements and translate them into robust, scalable AI-driven software solutions that bridge data science and production systems.
- Design and implement complex software systems for ML/AI applications, following best practices in software architecture, coding standards, and design patterns while ensuring seamless integration between data science experiments and production environments.
- Develop and maintain Python-based applications, libraries, and microservices using modern frameworks and tools, with a focus on transforming data science experiments into scalable production-ready AI services.
- Build and optimize robust model serving pipelines that enable both offline model training and real-time inference, ensuring high availability and performance.
- Automate end-to-end MLOps workflows and develop internal ML tools to streamline the machine learning lifecycle from experimentation to deployment.
- Monitor production data quality, model versions, cloud costs, and security compliance while maintaining infrastructure that empowers the data science team.
- Participate in code reviews, ensuring code quality, maintainability, and adherence to coding standards across both traditional software and ML pipeline codebases.
Other
- Mentor and guide junior developers and data scientists, fostering a culture of continuous learning and knowledge sharing in both software engineering and MLOps practices.
- Contribute to the development and implementation of automated testing strategies, including unit, integration, and end-to-end testing for both traditional applications and ML systems.
- Stay up to date with the latest trends, technologies, and best practices in the Python ecosystem, software engineering, and MLOps/AI infrastructure.
- Excellent communication and collaboration abilities.
- Strong problem-solving and analytical skills.
