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Hi, I'm Ashish Goyal

Machine Learning Engineer

Specializing in AI Agents and RAG systems. Building production agentic platforms with Claude Agent SDK, LangGraph, and MCP, and sharing deep technical implementations from first principles.

About Me

I'm a Machine Learning Engineer specializing in AI Agents and Retrieval-Augmented Generation (RAG). Currently at Verloop.io, I'm shaping the team's agent-platform strategy and building production RAG systems that power autonomous customer support for enterprise clients across e-commerce, banking, and healthcare.

I work hands-on with modern agentic frameworks — Claude Agent SDK, LangGraph, and Deep Agents — alongside Model Context Protocol (MCP), function calling, structured outputs, sub-agent orchestration, human-in-the-loop approval flows, and agent observability. On the retrieval side, I design evaluation frameworks for embedding models, cross-encoder rerankers, and late-interaction models, with production pipelines for complex document parsing, Weaviate vector search, and context engineering.

I also believe in understanding AI systems at a fundamental level. Through my technical blog, I break down topics like attention mechanisms (MHA, GQA, MLA), KV-cache, the LLaMA-2 architecture, and Mixtral 8×7B MoE, providing complete PyTorch implementations from scratch. I focus on building with clarity, sharing knowledge openly, and making advanced ML techniques accessible to practitioners.

At a Glance

4+
Years of Experience
AI Agents
Build & Orchestrate
RAG
Production Retrieval Systems
Agentic AI Engineer
RAG Systems
MCP & Tool Use
Actively Building

Skills & Expertise

Technologies and areas I work with

LLM & AI

Large Language ModelsPrompt EngineeringContext EngineeringFunction CallingStructured OutputsLLM EvalsTransformer Architecture

Agents & RAG

Claude Agent SDKLangGraphDeep AgentsModel Context Protocol (MCP)RAG SystemsVector DatabasesWeaviateEmbedding ModelsRerankersLate Interaction Models

ML Frameworks

PyTorchHugging Face TransformersvLLMScikit-learnXGBoostWeights & BiasesOptuna

Infrastructure

DockerKubernetesgRPCKafkaRabbitMQRedisPostgreSQLMySQL

Languages & Web Frameworks

PythonSQLFastAPIAsync ProgrammingGradio

Cloud & DevOps

Google Cloud PlatformCI/CDGitHub ActionsGit

Expertise

Agentic AI SystemsRetrieval-Augmented GenerationDeep LearningNatural Language ProcessingMicroservices Architecture

Experience & Education

My professional journey and key achievements

SDE - Machine Learning

Verloop.io

Oct 2024 - Present

Building agentic AI platforms and production RAG systems for autonomous customer support at enterprise scale.

  • Leading a comparative evaluation of agentic frameworks (Claude Agent SDK, LangGraph, Deep Agents) to define the team's agent-platform strategy — benchmarking tool calling, MCP server integration, state and memory persistence, human-in-the-loop approval flows, sub-agent orchestration, and skills invocation, alongside observability and evals.
  • Built a robust document processing pipeline for RAG systems to handle complex PDFs (tables, structured layouts) using OCR, improving contextual retrieval quality for enterprise clients across e-commerce, banking, and healthcare.
  • Designed a comprehensive retrieval evaluation framework benchmarking embedding models, cross-encoder rerankers, and late-interaction models — achieving 15% improvement in Recall and 5% gain in MRR while optimizing context windows to reduce latency and API costs.
  • Engineered production microservices using Python, Docker, Kubernetes, and gRPC with async Kafka/RabbitMQ pipelines, Weaviate vector search, and PostgreSQL/MySQL/Redis persistence for high-throughput query handling.

Machine Learning Engineer

Monsoon CreditTech

Feb 2022 - Sep 2024

Developed ML models for credit risk assessment in fintech.

  • Led development of a credit risk model using XGBoost with Bayesian optimization on bureau and demographic data, reducing delinquency rates by ~40% for a major bank's personal loan portfolio.
  • Implemented a model monitoring suite with drift detection (PSI, KS test) and SHAP-based explainability, enabling root cause analysis of model degradation in production.
  • Deployed scalable REST APIs using FastAPI, containerized with Docker and orchestrated on GCP Cloud Run for seamless model serving and infrastructure management.

Bachelor of Technology in Computer Science & Engineering

BML Munjal University

Jul 2016 - Aug 2020

Major in Computer Science and Engineering. Published research in Nature Scientific Reports.

  • Published: "Machine learning predicts live-birth occurrence before IVF treatment" in Nature Scientific Reports (2020)
  • Focused on Machine Learning, Deep Learning, and Software Engineering

Get In Touch

I'm always interested in discussing AI agents, RAG systems, LLMs, ML engineering, or potential collaborations. Feel free to reach out!

Or send me an email directly at ashishgy77@gmail.com