Amandeep Singh
Founder & CEO Welzin.ai
Chandigarh, India
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Aman is a seasoned Data & AI Architect with 12+ years of experience designing large-scale analytics, ML, and GenAI platforms for enterprise environments. As a former Senior ML Engineer at PayPal, he built production systems that processed billions of transactions, giving him deep expertise in making AI work at scale.
Now, as the founder of Welzin, Aman helps organizations transform complex data and legacy platforms into modern AI capabilities that drive measurable business impact. His mission is simple: make advanced AI practical, accessible, and valuable for businesses of all sizes.
Area of Expertise
Topics
How to do deep contextual tracing of Agentic decisions with OpenSearch
AI agents rely on LLM reasoning or vector similarity to decide and choose tools. But these approaches lack deterministic control, explainability and low-latency guarantees. In real production systems, agents need a fast and reliable way to decide which tool to invoke based on structured rules and historical context.
With this, Agentic observability has emerged as a critical discipline in 2026, shifting from simple logging to in-depth tracing of non-deterministic, multi-step workflows. Building a reliable GenAI app monitoring strategy for tracing the reasoning chain, now becomes more important from an observability pov.
In this talk, we will demonstrate how we can understand AI Agent decisioning for tool calling and further reasoning with OpenSearch. We will showcase how OpenSearch indexes agent logs, tool call metadata, execution constraints and past outcomes.
We will showcase a live hands-on demo of OpenSearch ML, indexing the AI Agent logs-metrics and further use them to comprehend the logs to understand the functioning and reasoning of AI agents.
Confused about how AI Agents are making decisions ? Then this talk is for you !
Deep Contextual Tracing of Agentic Decisions with Apache Lucene
AI agents often rely on LLM reasoning or vector similarity to choose tools, but these approaches lack deterministic control, explainability and low-latency guarantees. In real production systems, agents need a fast and reliable way to decide which tool to invoke based on structured rules and historical context.
In this talk, we will demonstrate how Apache Lucene can be used to understand AI Agents decisioning for tool calling and output generation. We will showcase how Lucene indexes agent logs, tool metadata, execution constraints and past outcomes. We will also show how Lucene can be used to filter, analyze and score agent tool calls at runtime.
We will show a live hands-on demo of how Apache Lucene can be used to store logs-metrics and how to comprehend these logs to understand the functioning of AI agents.
Attendees will also get GitHub templates and code samples for the complete demo implementation.
LLMs as a Judge: using LLMs and Evaluation frameworks for model improvement
In this 1.5 hours hands on workshop, you will learn how to design and implement a evaluative LLMOps pipeline. You will learn how to Implement feedback loop from live monitoring, back into the evaluation pipeline.
We will do a hands on workshop on a real world example of a RAG pipeline and integrate open source evaluation framework like RAGas, Evidently, Langsmith and Opik.
We will also demonstrate how to rigorously evaluate the model outputs, monitor their behavior and implement human in the loop assessment for continuous model improvement.
We will also cover:
1. Why LLM as a Judge works and when (and when not) to use it.
2. How to write evaluation prompts for binary scoring, chain of thoughts and structured output.
3. How to manage bias, verify against human ground truth and pitfalls in scaling and implementing evaluation methods in LLMOps pipeline.
KMCP and the Future of Agent-Native Kubernetes Control Planes
As AI agents become part of production platforms, their interaction with Kubernetes is increasingly ad hoc. Each agent embeds its own assumptions, permissions, and execution logic, making behaviour difficult to reason about, audit, or operate safely at scale.
KMCP introduces a missing control plane interface, a standardized contract for how agents exchange intent, context, and actions with Kubernetes systems.
This session explores how KMCP can be used as an infrastructure primitive rather than an AI feature. We’ll walk through practical platform scenarios where KMCP sits between agent logic and Kubernetes APIs, defining clear boundaries for execution, validation, and observability.
In this session, you’ll see how KMCP enables platform teams to :
Standardize agent-to-cluster interactions across tools, controllers, and workflows
Separate agent intent from execution logic, reducing tight coupling with Kubernetes internals
Enforce permission and policy boundaries before actions reach the control plane
Make agent actions observable and auditable using existing cloud-native telemetry
Create interoperable agent ecosystems aligned with CNCF principles
The result is a shift from opaque, agent-driven automation to predictable, contract-based operations. By treating KMCP as a control plane interface, platform teams gain the same guarantees they expect from APIs, controllers, and CRDs - applied to AI-driven systems.
This session is intended for platform engineers, Kubernetes practitioners, and architects designing the next generation of AI-native infrastructure.
TinyML at the Edge: Deploying and Optimizing AI Workloads on Zephyr RTOS
TinyML is transforming edge computing by enabling smart inference directly on microcontrollers but resource limitations make deployment complex. Zephyr RTOS lightweight, modular, and feature-rich is becoming a go-to platform for building embedded AI systems. This session walks through how to effectively run TinyML workloads on Zephyr using various inference engines like TensorFlow Lite Micro, microTVM, emlearn, and LiteRT, along with decision points for selecting runtimes based on hardware constraints. We will explore the runtime and how it simplifies AutoML workflows while supporting multiple backends. Attendees will also learn to use Zephyr’s Linkable Loadable Extensions (LLEXT) for hot-swapping models without reflashing. Performance optimization techniques such as quantization and operator fusion will be covered, along with benchmarking on physical devices vs Renode simulation. The talk concludes with real-world examples like health monitors and predictive maintenance, best practices for OTA model updates, and the future of embedded AI with Zephyr.
GPU‑Accelerated Workloads on KubeVirt: Scaling ML/AI in Kubernetes
KubeVirt is redefining how we run virtual machines in Kubernetes but what happens when those VMs need GPU acceleration for demanding AI/ML workloads?
In this lightning talk, I will walk through how to enable GPU-backed virtual machines in KubeVirt, and why this approach is gaining traction for secure, scalable, and isolated inference pipelines. We will explore the differences between container-based and VM-based GPU allocation, and see how KubeVirt integrates with CNCF tools like Prometheus and Kubernetes scheduler to monitor and optimize performance. If you are looking to push KubeVirt beyond typical VM use cases and into production-ready ML/AI workloads, this session will give you the technical foundation and the inspiration to get started.
Cloud Native Artificial Intelligence:Building Self-Healing Cloud Native Infrastructure with AI
This session explores how combining artificial intelligence and cloud native infrastructure can transform present Kubernetes operations into intelligent and self-managing platforms. Attendees will learn about how implementing AI-driven resource optimisation, predictive scaling, and automated incident response in cloud native environments will help organisations. This will equip machine learning engineers and data scientists with the knowledge to understand the changing Cloud Native Artificial Intelligence (CNAI) ecosystem and its opportunities.
KCD Sri Lanka 2025 Sessionize Event
KubeVirt Summit 2025 Sessionize Event
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