Building production-grade systems at the intersection of finance, AI, and distributed architectures
I architect and build high-performance systems spanning quantitative finance, machine learning pipelines, and full-stack applications. My work focuses on production-ready solutions that bridge complex backend infrastructure with intuitive user experiences.
- 🔬 Current Focus: Quantitative trading systems, ML-driven analytics, and distributed architectures
- 🏗️ Building: Real-time data pipelines, AI-powered APIs, and scalable web applications
- 🎯 Expertise: Full-stack development, ML deployment, Docker orchestration, and API design
- 📊 Interests: Algorithmic trading, NLP/LLM applications, and maritime tech
- 💡 Philosophy: Turning complex problems into elegant solutions
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Core ML/AI Skills:
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Advanced AI:
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Quantitative Finance:
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Trading Tech Stack:
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Backend Development:
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System Design:
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Data Science:
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Tools & Techniques:
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DevOps Stack:
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Infrastructure:
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Full-Stack Development:
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Engineering Practices:
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Database Technologies:
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Database Design:
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Product Skills:
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Technical PM:
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| Skill Category | Proficiency |
|---|---|
| 🐍 Python Development | |
| 🤖 AI/ML Engineering | |
| ⚛️ React & Frontend | |
| 📊 Quantitative Finance | |
| 🐳 Docker & DevOps | |
| 🗄️ Database Design | |
| ⚡ FastAPI & Backend | |
| 📈 Data Science | |
| ☁️ Cloud Architecture | |
| 🔧 System Design |
class PushkarKumarVats:
def __init__(self):
self.name = "Pushkar Kumar Vats"
self.role = "Full-Stack Engineer | Quant Developer | AI/ML Specialist"
self.location = "India 🇮🇳"
def current_work(self):
return {
"Quantitative Systems": [
"High-Frequency Trading (HFT)",
"Algorithmic Trading Engines",
"Risk Analytics & Portfolio Optimization"
],
"AI/ML Engineering": [
"Large Language Models (LLMs)",
"NLP Pipelines & Retrieval Systems",
"Model Deployment & MLOps",
"Computer Vision Systems"
],
"Full-Stack Engineering": [
"Real-Time WebSocket Architectures",
"Microservices & Distributed Systems",
"API Gateways & Service Mesh",
"Cloud-Native Applications"
],
"Infrastructure": [
"Kubernetes Orchestration",
"Docker Performance Optimization",
"CI/CD Automation",
"Scalable Cloud Infra (AWS/GCP)"
]
}
def learning_next(self):
return [
"Advanced C++ for Ultra-Low-Latency Systems",
"Distributed Systems Internals & CAP Theorem",
"Rust for High-Performance Computing",
"Real-Time Event Streaming (Kafka)",
"Advanced Quant Research & Strategies"
]
def tech_stack(self):
return {
"Languages": [
"Python", "TypeScript", "JavaScript", "C++", "SQL", "Bash"
],
"AI/ML": [
"PyTorch", "TensorFlow", "Scikit-learn", "Pandas", "NumPy"
],
"Backend": [
"FastAPI", "Node.js", "Express", "GraphQL", "gRPC"
],
"Frontend": [
"React", "Next.js", "Vite", "TailwindCSS"
],
"Databases": [
"PostgreSQL", "Redis", "MySQL", "Supabase"
],
"DevOps": [
"Docker", "Kubernetes", "AWS", "GCP", "Linux"
],
"Tools": [
"Git", "Pytest", "Jupyter", "Prisma"
]
}
me = PushkarKumarVats()
If you find my work valuable and want to support my open-source contributions and projects, consider buying me a coffee!
pushkarkumarvats@upi
