Description

Key Responsibilities

Design and implement highly available, low-latency model-serving architectures for real-time inference workloads.

Develop and standardize deployment patterns for scalable AI/ML services across Kubernetes-based platforms.

Lead API-based inference service design, integration, deployment, and lifecycle management.

Optimize model-serving performance through latency tuning, caching strategies, autoscaling, and resource management.

Establish monitoring, observability, SLOs/SLAs, alerting, and operational runbooks for production services.

Drive load testing, capacity planning, resiliency engineering, and disaster recovery readiness.

Integrate inference platforms with CI/CD pipelines to enable automated deployments and controlled releases.

Collaborate with Data Science, Platform Engineering, MLOps, and Infrastructure teams to ensure reliable production model operations.

Establish operational best practices, security standards, reliability measures, and performance benchmarks for enterprise AI deployments.

Required Technical Skills

Strong experience with online inference and production model-serving architectures.

Expertise in real-time APIs and distributed systems design.

Hands-on experience with Kubernetes, container orchestration, and service mesh technologies.

Knowledge of autoscaling, capacity management, and workload optimization.

Experience in performance engineering, load testing, and latency optimization.

Strong understanding of monitoring, observability, logging, distributed tracing, and SLO management.

Experience with CI/CD, DevOps, and Infrastructure as Code (IaC).

Knowledge of reliability engineering, fault tolerance, and resiliency patterns.

Experience operating cloud-based and on-premises platforms.

Proficiency in Python, Java, Go, or similar backend programming languages.

Preferred Qualifications

Experience with MLOps platforms and enterprise AI deployment frameworks.

Hands-on experience with real-time recommendation, fraud detection, risk assessment, personalization, or predictive analytics platforms.

Familiarity with GPU-based inference, model optimization, and multi-cloud deployments

Key Skills

Education

Any Graduate