Alexander Chernov
🤖 Link-Think-Act · Associate Principal Data Engineer @ AstraZeneca · Agentic Datasets & Intelligent MES · AgentOps · Cloud-Native / Hybrid Architectures · M.Sc. Physics - ICT
Toronto, Canada
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As a Subject Matter Expert, I bring domain-spanning knowledge with strong interdisciplinary problem-solving capabilities in frontend development, distributed systems, data analytics, cloud architecture, and system reliability. My focus lies in leveraging intelligent agents, advanced developer tooling, and GPU-accelerated platforms to build true scalable, self-adaptive systems.
This includes designing solutions that emphasize reliability, security, self-isolation, and infrastructure-as-code principles, using custom-defined resources and declarative stateful mapping. I apply practices from AIOps, MLOps, DevSecOps, and Site Reliability Engineering (SRE) to ensure operational excellence and long-term maintainability.
I have extensive experience implementing near real-time metrics, telemetry, and event tracking pipelines, processing terabytes of data across diverse environments. My background includes architecting distributed computing systems capable of handling large-scale data workloads with a focus on efficiency, scalability, and fault tolerance.
In the banking and finance domain, I have developed software centered on reactive streams, performance analytics, resilient event handling, and interactive dashboards. In the pharmaceutical and biotech sectors, I’ve contributed to Manufacturing Execution System (MES) platforms aligned with Good Manufacturing Practice (GMP) standards. Additionally, I’ve supported robotics and automation solutions for microchip fabrication (CPU, NAND), including FOUP transport and management systems, with deep technical expertise in SECS and HSMS protocols.
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Agentic Datasets on AKS: Data Products with Active Workflows
This session presents Agentic Datasets, a cloud-native pattern where datasets act as intelligent, self-managing data products on AKS. Each dataset runs as a DatasetAgent Custom Resource encapsulating metadata, reasoning logic, and executable workflows defined through a lightweight SDK. Workflows are distributed as signed OCI bundles and executed securely within the cluster, enabling automated validation, embedding generation, and lineage tracking without external orchestration.
Using controllers and GitOps pipelines, Agentic Datasets integrate seamlessly with service mesh, observability, and governance frameworks. The poster illustrates how datasets can reason about their own content, apply LLM-assisted automation for summarization or decision tasks, and participate as active, autonomous workloads in AI-enabled Kubernetes environments.
The design integrates with Azure OpenAI Service and Azure AI Foundry for LLM-assisted reasoning, enabling DatasetAgents to perform contextual analysis, summarization, and autonomous decision tasks directly from within Azure-hosted workflows.
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