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Forward Deployed Engineer

Uday Kumar M

Applied AI & Agentic Systems

AI engineer building production-grade AI systems — RAG pipelines, real-time voice agents, and multi-agent orchestration — on top of solid backend and cloud engineering across fintech and supply chain.

View my workResume

  • Applied AI

    RAG · Voice agents · Multi-agent systems

    Designing retrieval pipelines, real-time voice agents, and multi-agent workflows that automate real enterprise processes.

    • LLMs
    • RAG
    • LangChain
    • MCP
    • STT / TTS
    • AI guardrails

  • Backend & Cloud

    Scalable distributed systems

    Building FastAPI, Spring Boot, and Go services, Kafka event pipelines, and Kubernetes deployments on AWS and Azure.

    • Python
    • Java
    • Go
    • Kafka
    • Kubernetes
    • AWS / Azure

About

Bridging machine learning and software engineering.

I'm an AI engineer with hands-on experience building production-grade machine learning systems, backend services, and cloud-native data solutions across fintech and supply chain domains. I design and deploy intelligent systems — RAG pipelines, voice agents, multi-modal AI systems, and agent orchestration frameworks — alongside ML pipelines processing 1M+ records, distributed microservices, REST APIs, ETL workflows, and cloud-native deployments on AWS and Azure.

I enjoy solving complex engineering challenges and building AI-driven solutions that create measurable business impact.

Open toAI EngineerMachine Learning EngineerBackend EngineerData Engineer

  • Currently

    Forward Deployed Engineer Intern

    qBotica

  • Previously

    Software Engineer

    JPMorganChase

  • Focus

    RAG · Voice AI · Multi-agent systems

    Applied AI

  • Scale

    1M+ records · 3x peak throughput

    ML pipelines · Kafka pipelines

I build AI that works in the real world. Retrieval pipelines that find the right answer. Voice agents that talk back in real time. Teams of agents that run whole workflows. All on backends engineered to scale.

By the numbers

3x

throughput under peak load

Kafka event-driven pipelines · JPMorganChase

1M+

records through ML pipelines

Production machine learning systems

4

professional certifications

Including Claude Certified Architect

Selected work

Systems built for production.

Highlights from my engineering work at qBotica and JPMorganChase — from retrieval and voice to event-driven backends.

  • 01

    qBotica

    Enterprise RAG Assistants

    Retrieval-Augmented Generation pipelines that power context-aware AI assistants over enterprise knowledge, built to improve response relevance.

    • RAG
    • LLMs
    • Python
    • FastAPI
  • 02

    qBotica

    Real-Time Voice Agents

    Conversational voice agents that chain speech-to-text, large language models, and text-to-speech for natural, real-time interactions.

    • Voice AI
    • STT
    • TTS
    • LLMs
  • 03

    qBotica

    Multi-Agent Orchestration

    Multi-agent and multi-model workflows that coordinate specialized AI agents to automate complex business processes.

    • AI Agents
    • Multi-model
    • MCP
  • 04

    JPMorganChase

    Event-Driven Supply Chain Pipelines

    Kafka-based event-driven pipelines for supply chain operations, increasing throughput by 3x under peak load.

    • Kafka
    • Java
    • Spring Boot
    • Kubernetes
  • 05

    JPMorganChase

    High-Performance Go Services

    Go microservices and gRPC services for high-performance internal platforms, containerized for zero-downtime deployments.

    • Go
    • gRPC
    • Docker
    • Helm

Experience

From backend systems to applied AI.

Three roles across enterprise automation, university research, and global financial services.

  1. —

    Arizona, United States

    qBotica

    Current

    Forward Deployed Engineer (FDE) Intern

    • Designed and developed enterprise Retrieval-Augmented Generation (RAG) pipelines to deliver context-aware AI assistants and improve response relevance.
    • Built real-time AI voice agents integrating speech-to-text, large language models, and text-to-speech for natural conversational interactions.
    • Implemented multi-agent orchestration and multi-model workflows to automate complex business processes and coordinate specialized AI agents.
    • Developed Python and FastAPI services and REST APIs to integrate AI capabilities with enterprise applications and backend systems.
    • Contributed to AI governance and context engineering initiatives, exploring runtime guardrails, observability, evaluation, and auditability for enterprise AI applications.
    • Collaborated with cross-functional teams to develop, test, troubleshoot, and optimize AI solutions for real-world business use cases.

  2. —

    Tempe, Arizona, United States

    Arizona State University

    AI Research Assistant

    • Contributed to AI-focused research and cloud-based engineering initiatives.
    • Supported the development and evaluation of AI/ML solutions for research and engineering problems.
    • Assisted with data processing, model experimentation, performance evaluation, and analysis of research results.
    • Applied Python and machine learning frameworks to build, test, and improve AI-driven solutions.
    • Supported cloud infrastructure, deployment workflows, and automation for research applications.
    • Collaborated with faculty and research team members to investigate technical approaches, troubleshoot issues, and document findings.

  3. —

    India

    JPMorganChase

    Software Engineer (AI-Integrated Backend Engineer)

    • Developed Java Spring Boot microservices and distributed backend systems supporting supply chain operations.
    • Architected Kafka-based event-driven pipelines, increasing throughput by 3x under peak load.
    • Built Go microservices and gRPC services for high-performance internal platforms.
    • Containerized services using Docker, Helm, and Kubernetes, enabling zero-downtime deployments.
    • Contributed to scalable cloud-native architectures with a strong focus on performance and reliability.

Skills & technologies

A toolkit for intelligent systems.

Generative AI & Agents

11
  • LLMs
  • RAG
  • AI Agents
  • Multi-agent orchestration
  • Voice AI (STT / LLM / TTS)
  • Multi-modal AI
  • LangChain
  • Model Context Protocol (MCP)
  • Claude Code
  • Context engineering
  • AI guardrails & evaluation

Data Engineering

02
  • ETL pipelines
  • Data processing

Machine Learning & MLOps

03
  • Machine Learning
  • ML pipelines
  • Model experimentation & evaluation

Backend & Distributed Systems

06
  • FastAPI
  • Spring Boot
  • REST APIs
  • gRPC
  • Microservices
  • Kafka / event-driven

Languages

04
  • Python
  • Java
  • Go
  • SQL

Cloud & DevOps

05
  • AWS
  • Azure
  • Docker
  • Kubernetes
  • Helm

Education & certifications

Learning that compounds.

Uday Kumar M walking along a lit campus path at night, carrying his graduation gown, cap, and roses
Class of 2026

M.S. Data Science · Arizona State University · 2026

Arizona State University

Master of Science in Data Science

Aug 2024 — May 2026

Sri Venkateswara College of Engineering, Tirupati

Bachelor's degree, Computer Science

Jul 2020 — Apr 2024

Certifications

  • Claude Certified Architect (CCAF)

    Anthropic

  • Google Cybersecurity

    Google

  • Google IT Support Professional Certificate

    Google

  • Google Data Analytics Professional Certificate

    Google

Contact

Let's build something intelligent.

Open to AI, machine learning, backend, and data engineering roles. The fastest way to reach me is email.

umuncip@asu.edu
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