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Utsav Punia

Utsav Punia

AI/ML Engineer

Talent
AdelaideMember since August 20, 2026
PythonLangChainLangGraphMCP ServersCopilotKitA2UIGenerative UIStateGraphReactData PipelinesAI Systems Integration
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PythonLangChainLangGraphMCP ServersCopilotKit

About

I am an AI/ML Engineer who believes the best technology is built around real data, real users, and real problems. At SAGE Automation, I worked as the sole engineer on an agentic AI system that connected industrial data with intelligent agents capable of reasoning, computing, and surfacing insights in real time. I bring a strong blend of technical depth and human curiosity, comfortable in the code, comfortable in the room, and genuinely motivated by building solutions that matter.

Currently seeking full-time roles in Agentic AI Engineering, Applied AI, and AI Systems Integration across Australia.

Experience

AI/ML Intern – Agentic AI & Systems Integration at SAGE Automation (2026-02 – 2026-06)

  • Engineered a production-grade deep agent system analysing industrial tag data streams in real time – including alarm system data, fault signals, and process control metrics – with simultaneous multi-tag querying across Ignition/SCADA-connected environments
  • Designed agentic LLM workflows across 3 MCP servers using LangChain and LangGraph, enabling agents to autonomously select, orchestrate, and execute tools for real-time industrial data queries
  • Architected multi-agent StateGraph pipelines coordinating 2 specialised sub-agents, resolving 5 critical integration issues and implementing query batching to eliminate multi-tag query timeouts
  • Delivered a Generative UI layer via CopilotKit, AG-UI, and A2UI – streaming 6+ dynamic chart types including Pareto analysis for industrial alarm management directly to frontend in real time
  • Presented project architecture to cross-functional Sage stakeholders; authored intern onboarding documentation covering system setup, 5 resolved issues, and all 6 sprint deliverables

AI/ML Intern at Hoping Minds (2024-02 – 2024-08)

  • Developed an adaptive question bank engine for an LMS, dynamically adjusting question difficulty based on real-time learner performance data via rule-based AI logic
  • Streamlined learner content delivery by engineering Python data pipelines processing interaction metrics and personalising question sequencing platform-wide
  • Communicated weekly progress to cross-functional stakeholders, coordinating frontend and backend integration to drive end-to-end testing and production validation over 6 months

Implementation Analyst Intern at MoEngage (2023-04 – 2023-11)

  • Deployed MoEngage's SaaS marketing platform across client systems on Android, iOS, and React stacks, streamlining onboarding workflows and accelerating product adoption
  • Coordinated API integration delivery with client technical teams, resolving implementation issues and maintaining clear communication across 7 months of client-facing engagements

Education

Master of Artificial Intelligence and Machine Learning in Artificial Intelligence and Machine Learning – Adelaide University (2024 – 2026)

Bachelor of Computer Science and Engineering in Computer Science and Engineering – Chitkara University (2020 – 2024)

Skills

AI/ML Engineer

Reviews

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