ML Systems Engineer · Research-to-Production AI
Building AI systems that scale from research to real-world deployment.
I build production-grade AI systems that operate under real-world constraints — with a focus on latency, reliability, scalability, and deployment.
Distributed RL · Agentic AI · LLM-powered systems
01intent = parse(user_goal)02context = retrieve(knowledge, intent)03plan = agent.reason(intent, context)04action = controller.execute(plan)05monitor(action, latency_budget)Selected Projects
Production-scale AI systems designed and deployed under real-world constraints.
PROJECT / TELECOM AI
AI-Native Link Adaptation
Production AI for real-time 5G link adaptation under baseband latency constraints.
+20% throughput +10% spectral efficiency
PROJECT / RL SYSTEMS
High-Throughput Distributed RL Training System
Scalable RL infrastructure for large-scale experience generation and policy optimization.
20× faster training
PROJECT / AGENTIC AI
Agentic AI for Autonomous Networks
Intent-driven agentic AI that translates high-level network intents into closed-loop control actions.
Pareto-aware control
Research
Peer-reviewed publications in machine learning, control, and network optimization.
Blog
Technical deep-dives on distributed RL, agentic AI, and systems engineering.
About
I build AI systems for environments where models do not get unlimited time, data, or compute.
My work sits at the intersection of reinforcement learning, agentic AI, control, and distributed systems — with a focus on turning research ideas into reliable production systems for real-world networks.
Current
Master Researcher in Machine Learning
Ericsson AB · Dec 2023 – Present
Let’s build AI systems that survive production constraints
If you are working on production RL, agentic AI systems, autonomous networks, or ML infrastructure under real-world constraints, I’d be happy to connect.