ML integration Engineer

5-7 Years

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Early Applicant

Job Description

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