Distributed Systems Engineer, Data & Inference Platform

About the role

The role is part of a growing team focused on building trustworthy AI solutions. The position involves working with cutting-edge technologies such as RabbitMQ, Kubernetes, Apache Kafka, Amazon Web Services, and more.

Responsibilities

• Develop and maintain scalable and reliable systems using distributed technologies.
• Collaborate with cross-functional teams including product, engineering, and sales to deliver high-quality AI solutions.
• Contribute to the design and implementation of machine learning models and algorithms.
• Perform code reviews and mentor junior engineers.
• Stay updated with the latest advancements in AI and related technologies.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• At least 3 years of relevant work experience in software development or AI.
• Experience with at least one of the following: RabbitMQ, Kubernetes, Apache Kafka, Amazon Web Services, Hadoop, Fiddler, Airflow, Microsoft Azure, Apache Spark.
• Strong understanding of artificial intelligence concepts and technologies.
• Proficiency in Python, Java, or another relevant programming language.
• Excellent problem-solving skills and ability to work independently and collaboratively.

Qualifications

• Experience with machine learning frameworks like PyTorch, TensorFlow, or others.
• Knowledge of data structures, algorithms, and software design patterns.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong communication and interpersonal skills.

Skills

• Experience with distributed systems and microservices architecture.
• Hands-on experience with data processing pipelines.
• Understanding of security best practices and compliance standards.
• Ability to work in a fast-paced, agile environment.

Benefits

Competitive compensation package, comprehensive health benefits, flexible work arrangements, professional development opportunities, and a supportive company culture.

Pay

USD 164,000 / year

Schedule

Hybrid