ML integration Engineer
1 week ago
- Design and implement scalable ML pipelines for Generative and Agentic AI applications.
 - Integrate AI models into production environments using containerized platforms such as OpenShift and Kubernetes.
 - Collaborate with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.
 - Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.
 - Ensure seamless deployment and integration of AI services in cloud environments (e.g., AWS, Azure, GCP).
 - Implement and maintain network security protocols to safeguard AI systems and data pipelines.
 - Monitor and optimize system performance, reliability, and scalability.
 - Support CI/CD processes and infrastructure for AI model deployment and updates.
 
Requirements
- 5+ years of experience in Machine Learning engineering or AI system integration.
 - Hands-on experience with OpenShift, Docker, Kubernetes.
 - Knowledge of cloud platforms (e.g. AWS, GCP) is a must-have.
 - Exposure to data and network security and compliance in AI systems.
 - Understanding of Generative AI and Agentic AI concepts.
 - Experience with LLM prompt engineering, or RAG pipelines.
 - Knowledge of API integration and microservices architecture.
 - Proficiency in Python used both for ML and automation tasks
 - Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
 - Knowledge of Workflow Orchestrator, such as Ctrl-M
 - Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
 - Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, OpenTelemetry.
 
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