Artificial Intelligence Engineer

Role & responsibilities

Implement and manage AIOps platforms for intelligent monitoring, alerting, anomaly detection, and root cause analysis (RCA).

• Possess end-to-end knowledge of VLLM model hosting and inferencing.
• Advanced knowledge of public cloud platforms such as AWS and Azure.
• Build and maintain machine learning pipelines and models for predictive maintenance, anomaly detection, and noise reduction.
• Experience in production support and real-time issue handling.
• Design dashboards and visualizations to provide operational insights to stakeholders.
• Working knowledge of Bedrock, SageMaker, EKS, Lambda, etc.
• 1 to 2 years of experience with Jenkins and GoCD to make build/deploy pipelines.
• Hands-on experience with open-source and self-hosted model APIs using SDKs.
• Drive data-driven decisions by analyzing operational data and generating reports on system health, performance, and availability.
• Basic knowledge of kserve and rayserve inferencing .
• Good knowledge of high level scaling using Karpenter , Keda , System based vertical/horizontal scaling.
• Strong knowledge on linux operating system or linux certified .
• Previous experience with Helm chart deployments and Terraform template and module creation is highly recommended.

Secondary Responsibilities:

• Proven experience in AIOps and DevOps, with a strong background in cloud technologies (AWS, Azure, Google Cloud).
• Proficiency in tools such as Kubeflow, Kserve, ONNX, and containerization technologies (Docker, Kubernetes).
• Experience with enterprise-level infrastructure, including tools like terraform, helm, and On-Prem servers hosting.
• Previous experience in fintech or AI based tech companies are highly desirable.
• Demonstrates the ability to manage workloads effectively in a production environment.
• Possesses excellent communication and collaboration skills, with a strong focus on cross-functional teamwork.