. Bachelor's degree in Computer Science, Engineering, or a related field.
. 4+ years of experience in Machine Learning engineering or AI system integration.
. Bash and Unix/Linux command-line toolkit is a must-have.
. Hands-on experience with OpenShift, Docker, Kubernetes.
. Knowledge of cloud platforms (e.g. AWS) is a must-have.
. Exposure to data and network security and compliance in AI systems.
. Knowledge of API integration and microservices architecture.
. Proficiency in Python used both for automation and ML-related tasks
. Knowledge of Workflow Orchestrator, such as Ctrl-M
. Good knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
. Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, OpenTelemetry.
. Understanding of Generative AI (e.g. prompt engineering, RAG pipelines) and Agentic AI concepts.