Aryan Agarwal
Machine Learning Engineer
SeniorAI/ML Engineer
6years in AI/ML
7years in tech
2years leading teams
Overview
Quantitative researcher with 4+ years of experience building research-driven, data-intensive systems that combine statistical modeling and software engineering. Hands-on experience developing ML workflows for finance-focused forecasting and inference, and building GenAI/RAG solutions including retrieval, re-ranking, evaluation, and deployment with APIs. Focused on reliable, measurable AI systems for real-world use.
Phone
Timeline
Quantitative Researcher / Machine Learning Engineer
·
Middle
26 Miles Capital
·
Full-Time
Built ML-based analytics for earnings call and macro filing data, including summarization and sentiment scoring. Engineered forecasting pipelines generating 50+ alpha features such as order-flow toxicity and bid-ask imbalance, and improved model validation for better portfolio outcomes. Developed modeling and inference pipelines for multi-asset derivatives to automate position sizing and dynamic hedging while meeting strict latency SLAs.
Python
LLM
RAG
Statistics and Machine Learning Toolbox
PyTorch
Airflow
AI Agents
AWS
C++
Bash
Redis
Malaviya National Institute of Technology (MNIT Jaipur)
Bachelor's Degree ·
Electronics and Communication
Machine Learning Intern
·
Junior
Unacademy
·
Internship
Worked on ML models for customer query routing. Contributed to building and exposing model capabilities through APIs for downstream usage.
Python
ML.NET
PyTorch
TensorFlow
NLP
Projects
High-Frequency Alpha & Feature Engineering Engine
Developed a feature engineering pipeline for high-frequency market data to support predictive modeling. Processed large volumes of tick-level order book events to compute signals such as order book imbalance and flow toxicity. Produced automated performance reporting and risk analytics, using Python with NumPy, pandas-adjacent tooling, Redis caching, and visualization for monitoring.
Enterprise Agentic RAG Platform
Created an agentic RAG platform focused on improving contextual retrieval for enterprise use. Implemented retrieval with dense embeddings and BM25, plus fusion and cross-encoder re-ranking to improve answer relevance. Added dynamic routing, automated evaluation, and deployed the service with FastAPI and Docker while supporting conversation memory and semantic caching.
Technical skills
Languages
3
C++
C
Python
AI/ML
4
BERT
Embeddings
Model Context Protocol
AWS Bedrock
Other
24
AWS
Amazon S3
PostgreSQL
Apache Kafka
Agentic Workflows
LLM
FastAPI
LangChain
LangGraph
NumPy
Scikit-learn
Redis
Polars
Ragas
LightGBM
Qdrant
Matplotlib
GitHub
OpenAI
Docker
Rest API
AI Agents
Machine Learning
RAG
Job preferences
- Work permits
- India
- Citizenship
- India
- Salary
- from $70k per year
- Employment type
- Full-Time
- Locations
- Indi
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