We are looking for a Senior Python Developer with deep expertise in Python programming, event-driven architectures and large-scale data processing to lead one of the company's most important areas.
Role and responsibilities
You will lead design and optimize cloud-native applications, build high-performance data pipelines, and develop scalable solutions on AWS. Work with machine learning (ML) workflows, including LLM (Large Language Models) integration.
Requirements:
7+ years of Python development in production environments
1-2 years of experience managing a team, including leading, delegating, overseeing, and communicating
Tests – Proven experience writing automated tests with frameworks like Pytest, focusing on unit and integration testing
Strong understanding of OOP, functional programming, and performance optimization
Expertise in Pandas, NumPy, and PySpark for large-scale data processing
Experience with concurrent programming (multithreading, multiprocessing, asyncio)
Proficiency with monitoring tools such as Datadog and CloudWatch, including performance monitoring, log aggregation, alerting, and troubleshooting
Practical experience working with column-oriented databases like ClickHouse or Redshift, including optimizing complex queries for performance at scale
Event driven architecture – Hands-on experience with event-driven architectures and message queues (Kafka, SQS, SNS, EventBridge)
Work with AWS services (Lambda, Step Functions, Kinesis, S3, DynamoDB)
Optimize database interactions with SQL and NoSQL (DynamoDB, PostgreSQL, etc.)
Work with ML models, including LLM-based applications (Fine-tuning, inference, optimization)
7+ years of Python development in production environments
1-2 years of experience managing a team, including leading, delegating, overseeing, and communicating
Tests – Proven experience writing automated tests with frameworks like Pytest, focusing on unit and integration testing
Strong understanding of OOP, functional programming, and performance optimization
Expertise in Pandas, NumPy, and PySpark for large-scale data processing
Experience with concurrent programming (multithreading, multiprocessing, asyncio)
Proficiency with monitoring tools such as Datadog and CloudWatch, including performance monitoring, log aggregation, alerting, and troubleshooting
Practical experience working with column-oriented databases like ClickHouse or Redshift, including optimizing complex queries for performance at scale
Event driven architecture – Hands-on experience with event-driven architectures and message queues (Kafka, SQS, SNS, EventBridge)
Work with AWS services (Lambda, Step Functions, Kinesis, S3, DynamoDB)
Optimize database interactions with SQL and NoSQL (DynamoDB, PostgreSQL, etc.)
Work with ML models, including LLM-based applications (Fine-tuning, inference, optimization)
This position is open to all candidates.
