Hassan Masroor

1. Backend Engineering: Designed and deployed diverse backend architectures across multiple production systems
— spanning distributed microservices, monolithic multi-service, event-driven pipelines, and multi-tenant execution — with hands-on implementation of REST WebSockets, RPC, stateless
stateful session management, scheduler-queue mechanisms, and caching, integrated across relational, NoSQL,
and vector database systems.
2. ML Engineering: Executed full-spectrum ML engineering across generative models, large models,
multimodal architectures, computer vision, and signal processing — spanning models including LLaMA, Mistral,
GPT-series, GANs, and Diffusion Models — covering the complete pipeline from research and fine-tuning
(including RLHF) through quantization, optimization, and production inference deployment.
3. Agentic AI & Automation: Designed and deployed end-to-end agentic AI systems, multi-agent workflows, and
SaaS automation pipelines — spanning autonomous decision pipelines, multimodal interfaces (voice + text),
and complex orchestration layers for fully automated, zero-human-intervention operational workflows.

- Spearheading full-stack system design and process digitization for a SaaS-based operational platform in the private
aviation sector, aligning engineering, product, and operations stakeholders around a unified technical roadmap.
- Delivering specialized AI/ML solutions for computer vision and NLP challenges
- Developing optimized deep learning architectures for edge deployment
- Contributing to research initiatives in low-resource language processing and blockchain applications

Key Skills:
- Contributed to cyber-physical systems research with focus on multi-agent coordination and forecasting:
1. Developed multi-agent robotic navigation algorithms ensuring 100% safety compliance in dynamic environments
through advanced path planning and coordination strategies;
2. Built atmospheric-hydrologic forecasting systems integrating cyber-physical sensing networks with ML prediction
models.

- Focused on deep neural network optimization and model compression techniques
- Advanced state-of-the-art in few-shot learning for resource-constrained environments

- Developed algorithms for multi-agent robotic systems and safety mapping
- Contributed to atmospheric-hydrologic forecasting using cyber-physical systems

- Key Skills: Neural Network Compression, Knowledge Distillation, Quantization, Deep Learning
- Advanced state-of-the-art in neural network optimization and few-shot learning, with focus on:
1. Developed neural network compression techniques through knowledge distillation, pruning, and quantization,
achieving 70% model size reduction with