Machine Learning Integration Engineer

This ML / Integration Engineer role focuses on building and integrating Generative AI and Agentic AI solutions into enterprise environments. You will work closely with data scientists, architects, and DevOps teams to design, implement, and optimize AI pipelines and infrastructure.

Key Responsibilities:

• 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

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 4+ 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.