Knowledge of MLOps AIOps model monitoring and AI observability frameworks, Experience supporting Generative AI Machine Learning or AI platform initiatives, Analytics Dashboarding BI, Creating executive dashboards for GenAI insights usage performance cost reliability, Relevant cloud certifications Azure Data Engineer Databricks Snowflake, Data Engineering Pipeline Development, Building scalable batch and streaming pipelines for GenAI data telemetry usage model outputs

Orchestration, Distributed Systems, Debugging, execution history, idempotency, operational correctness, architectural principles, Coordination, recovery, policy-enforced execution, execution semantics, control planes, failure handling, technical decision frameworks, correctness under partial failure, workflow infrastructure, Scheduling, auditability, runtime observability, Reconciliation, execution visibility, retries, core infrastructure platforms, reliability-critical backend platforms, Compensation

Node.js, Kafka, Microservices, Sql, Nosql, Rabbitmq, Gcp, Distributed Systems, Python, AWS, backend optimization, event-driven architectures, caching strategies

Design Patterns, distributed internet systems, tech stack, Design Architecture, backend programming language, data-driven web application design

Java, Golang, Distributed Systems, Performance Tuning, Python, data models, production debugging, AI-assisted development, NoSQL databases

Apis, Test Strategy, Distributed Systems, AWS, Automation architecture, Infrastructure optimization, CI CD-driven Quality Engineering, quality engineering, Scalable automation frameworks, Cloud-native applications, Microservices architecture

Rtos, Distributed Systems, System Architecture, C++, Linux, Networking, Debugging, Device Drivers, Kernel, Embedded Systems

Jenkins, Devops, Git, Docker, Kubernetes, Python, AgenticAI frameworks, R, AI ML engineering, CI CD tools