Responsibilities
- Design, develop, and deploy secure, reliable, and scalable ML-based software products that deliver high availability and low latency for enterprise-level customers.
- Own end-to-end development, maintaining high standards in software design, coding, code reviews, automated testing, and deployment within CI/CD practices.
- Participate in code and architectural reviews, and write technical documentation to ensure high code quality and maintainable systems across distributed engineering teams.
- Optimize ML-based software components to fully leverage distributed system architectures, including parallel architectures, clusters, multicore SMPs, and GPUs.
- Work with the SRE team to identify and resolve technical challenges in the production environment.
- Collaborate with AI scientists to integrate algorithmic components into effective solutions and products.
- Partner with project and program managers to understand requirements and effectively address customers' business challenges.
Basic Qualifications
- Bachelor's degree in Computer Science, Engineering, or related technical field required.
- Minimum of 6 years of professional software development experience, with demonstrated ability to deliver highly scalable, performant, and reliable software solutions.
- Extensive programming expertise (6+ years) in at least one modern programming language such as Python, Java, C/C++, or Rust.
- Advanced proficiency in object-oriented software design principles and development methodologies.
- Demonstrated expertise in resolving complex technical challenges, supported by comprehensive knowledge of data structures, algorithms, and fundamental computer science principles (Operating Systems, Computer Architecture, Databases, Networking).
Preferred Qualifications
- Advanced degree (Master's or PhD) in Computer Science or a relevant technical discipline.
- Demonstrated experience across the comprehensive software development lifecycle, encompassing design architecture, implementation, code review processes, test automation, and CI/CD deployment methodologies.
- Substantial background in architecting and implementing large-scale distributed systems within cloud environments.
- Proficient technical expertise with major cloud platforms (AWS, Azure, GCP) and containerization technologies (Kubernetes, Docker).
- Comprehensive understanding of enterprise-grade big data frameworks and technologies (Hadoop, Impala, Spark, Flink, Airflow, Kafka, Redis, MongoDB, Cassandra).
- Practical experience developing and deploying machine learning or AI solutions utilizing industry-standard MLOps tooling (Ray, MLflow, Kubeflow) and data processing libraries (Pandas, Dask).
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