Rach C
Al/ML Engineer | LLMs, RAG, Multi-Agent Al |MLOps & Cloud (AWS | Azure | GCP) | RealTime Inference | Edge & On-Device Al | Model Optimization & Deployment | Seeking Full-Time Al/ML Engineering Roles
- Role
- Ai Ml Engineer at Northern Trust
- Location
- San Francisco, CA, US
- LinkedIn followers
- 500 followers
About Rach C
AI/ML Engineer with around 4 years of experience in the finance domain, delivering machine learning, LLM, and RAG-based AI solutions for portfolio intelligence, fraud detection, and credit risk analytics. Skilled in Python, PyTorch, XGBoost, LightGBM, Spark, Databricks, vector databases, and cloud platforms (AWS/Azure) to build scalable, real-time AI/ML pipelines. Proven track record of enhancing financial decision-making, reducing risk, and automating analytics workflows through data-driven AI/ML technologies.
Experience
Ai Ml Engineer
Feb 2025 — Present · US
Designed an AI-driven portfolio intelligence copilot using LLMs, RAG, and LangChain to analyze SEC filings and market data, reducing manual research time 45% across institutional asset-management teams. • Engineered a vector search architecture using Pinecone, FAISS, and pgvector to retrieve financial documents from Bloomberg and Refinitiv datasets, improving semantic investment insight discovery 60%. • Implemented agentic AI workflows using LangGraph and LlamaIndex to orchestrate SQL queries, ML pipelines, and financial document retrieval, accelerating portfolio insight generation for advisors 4x faster. • Developed predictive portfolio risk forecasting models using LSTM, Prophet, and ARIMA on Databricks with Spark pipelines, improving early volatility detection 35% for multi-asset institutional portfolios. • Optimized factor return prediction models with XGBoost and LightGBM on alternative market datasets and Bloomberg APIs, improving portfolio strategy insights 30%. • Evaluated financial sentiment models using FinBERT and HuggingFace Transformers on earnings and macroeconomic news, enabling real-time AI-driven investment advisory intelligence. • Accelerated large-scale LLM inference and ML training using NVIDIA CUDA, RAPIDS AI, and PyTorch on Azure Kubernetes clusters, enabling processing of 500K+ financial documents monthly.
Education
Saint Louis University
Master's of Science, Computers and Information systems
Girraj Government College, Nizamabad (5005)
Bachelor's degree, Computer Science
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