Source description
About the role
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or a related technical field; equivalent professional experience will also be considered.
7+ years of professional experience in software engineering, backend engineering, platform engineering, DevOps, MLOps, cloud engineering, or a related discipline, including experience supporting production systems.
Strong proficiency in Python and experience building maintainable services, APIs, internal tools, jobs, or workflow automation in production environments.
Experience designing, deploying, and supporting cloud-based applications in AWS environments, including hands-on experience with services such as Amazon Bedrock, Lambda, S3, IAM, CloudWatch, Glue, MWAA/Airflow, or similar technologies.
Experience with CI/CD pipelines, Git-based development workflows, automated testing, configuration management, and release practices.
Experience with Docker, Kubernetes or other containerized services, Terraform or Infrastructure-as-Code, and production monitoring/alerting tools.
Experience with workflow orchestration, data pipelines, or job scheduling tools such as Airflow/MWAA, Glue, Lambda, cron-based jobs, or equivalent technologies.
Working knowledge of SQL and relational databases such as PostgreSQL; experience with distributed data or search systems is a plus.
Experience building or supporting AI-enabled applications using LLM APIs, retrieval-augmented generation (RAG), knowledge bases, AI agents, or similar technologies.
Strong troubleshooting skills, including production issue triage, root-cause analysis, log analysis, and implementation of durable solutions.
Ability to partner effectively with domain experts and translate workflow needs into practical, maintainable technical solutions.
Strong communication, documentation, and collaboration skills in cross-functional environments.