Rajershi Meesala
AI/ML Engineer | 5+ yrs building startup scale AI products | LLMs, RAG, NLP, Fraud Detection | Python, PySpark, Databricks, MLOps | Scalable ML pipelines, realtime inference, production AI
- Role
- Ai Ml Engineer at Databricks
- Location
- San Francisco, CA, US
- LinkedIn followers
- 500 followers
About Rajershi Meesala
AI/ML Engineer with 5+ years of experience building, I specialize in turning ambitious ideas into production-ready products taking solutions from 0→1 and scaling them to 1→N across high-growth environments. deploying, and scaling high-impact AI systems that power real business outcomes.My expertise spans the full AI lifecycle: data engineering, feature development, model training, deployment, MLOps, and continuous optimization. I’ve designed realtime inference systems, large scale ML pipelines, and LLM powered applications that process millions of transactions with high reliability and low latency.Hands-on experience includes Python, PySpark, Databricks, MLflow, AWS SageMaker, distributed data systems, and CI/CD automation. I’ve built solutions across fraud detection, credit risk modeling, demand forecasting, NLP, and Retrieval-Augmented Generation (RAG) systems using LangChain, LlamaIndex, and OpenAI GPT models.I’m known for solving complex problems under ambiguity, moving fast, and owning outcomes end-to-end. I work effectively across engineering, product, and business teams to deliver scalable, measurable, and customer-focused AI solutions.Driven by innovation, execution, and continuous learning, I’m passionate about building the next generation of intelligent systems that create lasting impact.
Experience
Ai Ml Engineer
Jul 2024 — Present · CA, US
Developed LLM-powered document intelligence solutions using LangChain, LlamaIndex, OpenAI GPT-4, and RAG pipelines on Databricks, extracting insights from 40K+ monthly documents and reducing manual review by 35%.• Engineered an end-to-end demand forecasting pipeline using Databricks, MLflow, LightGBM, Prophet, and PySpark, reducing inventory overstock by 14% across 10M+ SKUs for a Fortune 500 retail client.• Built a real-time credit risk scoring system with PySpark, XGBoost, Delta Lake, and Databricks Model Serving, processing 250K+ daily transactions with sub-250ms inference latency.• Implemented MLOps CI/CD pipelines using Databricks Workflows, GitHub Actions, and MLflow Model Registry, reducing deployment cycles from 2 days to under 6 hours with full model lineage tracking.• Optimized data preprocessing and feature engineering pipelines using Python, NumPy, pandas, and TensorFlow, with C++ inference modules, improving pipeline runtime by 18% on large feature datasets.• Deployed and monitored ML models on AWS SageMaker using S3, EC2, and Lambda, reducing infrastructure costs by 15% through auto-scaling and spot instance optimization.• Collaborated with Data Engineering, Product, and DevOps teams in Agile/Scrum, delivering ML features on bi-weekly releases using Jira and Confluence, maintaining 95%+ on-time delivery across 6 sprints.
Education
University of Cincinnati
Master's degree, Information Technology
2024
Acharya Nagarjuna University
Bachelor of Technology - BTech, Information Technology
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