Senior Python Developer / Data Engineer / ML Pipelines
- Discuss this with your agent
- Open in Claude
- Open in ChatGPT
Role details
Tech stack
+12 more
Job description
Senior Python Developer / Data Engineer / ML Pipelines 100% Remote role Responsibilities Design and build large-scale data pipelines for ingestion, transformation and processing Work on ETL/ELT workflows handling different types of data Build and maintain end-to-end ML pipelines from data preparation to deployment and monitoring Collaborate with data scientists to productionize ML models Work on feature engineering, training pipelines and model serving Ensure data quality, monitoring and pipeline reliability Optimize systems for performance, scalability and cost Contribute to clean, maintainable, production-grade Python code
Requirements
8+ years of software engineering experience with Python as primary language Strong background in data engineering (ETL/ELT, pipelines, data processing) Hands-on experience building and maintaining ML pipelines in production environments Experience with PySpark / Apache Spark Experience with workflow orchestration tools like Airflow, Dagster, or Prefect Good understanding of streaming/data processing systems (Kafka, Kinesis, etc.) Experience working with cloud platforms (AWS / GCP / Azure) Strong SQL skills and experience with data warehouses Comfortable working in a distributed/remote engineering setup Plus Experience with NLP or LLM-based systems Familiarity with MLOps tools like MLflow, Kubeflow, or similar Experience with feature stores Exposure to data privacy, PII detection, or compliance-related systems
Apply for this position
This job is hosted externally. Click below to view the full posting and apply.
Apply on www.wayup.comGood distractions
Talks and stories from around this role — technically off-topic, practically not.
Moments
Explore playlistsVideos
See allRelated articles
See all
Highest Paying Tech Companies for Developers
The Most Popular IT Jobs on the Market
MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production
MLOps – What’s the deal behind it?