ML integration Engineer

• 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.