DEVELOPER
Building systems where machine intelligence, software architecture, and hardware operate as a single pipeline.
ABOUT
through work
I am an AI systems developer working at the intersection of machine learning, system architecture, and robotics.
My work centers on building intelligent, end-to-end systems from model design and signal processing to backend orchestration and hardware integration.
"Mathematics before abstraction. Systems over frameworks. Intelligence as infrastructure."
// Current Interests [LIVE]
FEATURED ARCHITECTURE
DYNAMIC
LOAD
BALANCER
A production-grade simulation engine for multiprocessor scheduling, featuring an autonomous Deep Q-Network (DQN) that learns optimal process distribution.
REINFORCEMENT LEARNING
Implements Double DQN with Prioritized Experience Replay to adapt to bursty workloads.
PERFORMANCE METRICS
Real-time tracking of Jain's Fairness Index, throughput, and CPU utilization.
# dqn_balancer.py
class DQNAgent:
def __init__(self, state_dim):
self.policy_net = DQNetwork(
hidden_layers=(256, 128),
dueling=True
)
self.memory = PrioritizedReplay()
def select_action(self, state):
with torch.no_grad():
q_values = self.policy_net(state)
return q_values.argmax().item()
FOCUS AREAS
#AI Multi-Agent Collaboration
Orchestrating autonomous agents for complex problem solving.
#Autonomous Dev Pipelines
Self-healing CI/CD and development workflows.
#Robotics–NLP Integration
Bridging large language models with physical actuation.
#Scalable AI Infrastructure
High-performance compute clusters and training environments.
#Math-Driven ML Design
First-principles approach to model architecture and optimization.