Real-Time Inference Engineering Lead (FTE / Hybrid)

NTT DATA is a global business and technology services company providing enterprise-scale AI, cloud, security, data, and application services. The Real-Time Inference Engineering Lead will design, build, and operationalize low-latency, resilient model-serving services for predictive AI use cases across cloud and on-premises environments. The role provides technical leadership for inference architecture, deployment, APIs, performance, observability, reliability, CI/CD, and production operations.

Responsibilities:

• Define the target architecture and engineering standards for real-time predictive model-serving services across cloud and on-premises environments
• Design, build, test, deploy, and operate scalable online inference services that meet latency, throughput, availability, resiliency, and security requirements
• Establish reusable model-serving patterns for synchronous APIs, asynchronous inference, batch-adjacent processing, and event-driven real-time use cases where appropriate
• Build standardized deployment approaches for predictive models, including model packaging, versioning, release promotion, canary deployment, rollback, and retirement
• Design and implement secure API patterns for inference services, including authentication, authorization, traffic management, rate limiting, auditability, and integration with enterprise systems
• Engineer Kubernetes-based serving platforms using GKE, OpenShift, and related container orchestration capabilities
• Implement autoscaling, resource allocation, quota management, capacity planning, and workload-isolation controls for variable inference demand
• Conduct performance engineering, load testing, stress testing, and failure testing to validate service behavior under expected and peak production workloads
• Identify and implement latency-optimization opportunities across model initialization, feature retrieval, network paths, API handling, runtime configuration, and infrastructure utilization
• Define and implement monitoring, telemetry, dashboards, alerts, SLIs, SLOs, and error-budget practices for real-time inference services
• Partner with ML platform, data engineering, application engineering, security, and operations teams to integrate model services with governed data, feature, network, and identity capabilities
• Implement CI/CD and automated validation for model-serving services, infrastructure configuration, APIs, performance benchmarks, and release-readiness checks
• Build operational runbooks, incident-response procedures, support models, and production-readiness artifacts for real-time services
• Drive reliability improvements through root-cause analysis, capacity reviews, resiliency testing, disaster-recovery planning, and continuous operational improvement
• Mentor engineers and establish reusable technical documentation, reference implementations, and knowledge-transfer materials for real-time inference capabilities