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I am an AIML Engineer with 4.5 years of experience building and deploying Generative AI and production-scale machine learning systems. I specialize in LLM fine-tuning (LoRA/SFT), hybrid Retrieval-Augmented Generation (RAG), and structured AI decision frameworks for enterprise applications. I have strong experience with AWS SageMaker, MLflow, real-time drift monitoring, and explainable AI in regulated environments. I focus on delivering scalable, low-latency, and cost-efficient AI solutions that align with business goals and compliance requirements.…I am an AIML Engineer with 4.5 years of experience building and deploying Generative AI and production-scale machine learning systems. I specialize in LLM fine-tuning (LoRA/SFT), hybrid Retrieval-Augmented Generation (RAG), and structured AI decision frameworks for enterprise applications. I have strong experience with AWS SageMaker, MLflow, real-time drift monitoring, and explainable AI in regulated environments. I focus on delivering scalable, low-latency, and cost-efficient AI solutions that align with business goals and compliance requirements.

Kalesha Vali Shaik

AI Engineer, AI Developer, Data Scientist, +2

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I am an AIML Engineer with 4.5 years of experience building and deploying Generative AI and production-scale machine learning systems. I specialize in LLM fine-tuning (LoRA/SFT), hybrid Retrieval-Augmented Generation (RAG), and structured AI decision frameworks for enterprise applications. I have strong experience with AWS SageMaker, MLflow, real-time drift monitoring, and explainable AI in regulated environments. I focus on delivering scalable, low-latency, and cost-efficient AI solutions that align with business goals and compliance requirements.…I am an AIML Engineer with 4.5 years of experience building and deploying Generative AI and production-scale machine learning systems. I specialize in LLM fine-tuning (LoRA/SFT), hybrid Retrieval-Augmented Generation (RAG), and structured AI decision frameworks for enterprise applications. I have strong experience with AWS SageMaker, MLflow, real-time drift monitoring, and explainable AI in regulated environments. I focus on delivering scalable, low-latency, and cost-efficient AI solutions that align with business goals and compliance requirements.

Available to hire

I am an AIML Engineer with 4.5 years of experience building and deploying Generative AI and production-scale machine learning systems. I specialize in LLM fine-tuning (LoRA/SFT), hybrid Retrieval-Augmented Generation (RAG), and structured AI decision frameworks for enterprise applications.

I have strong experience with AWS SageMaker, MLflow, real-time drift monitoring, and explainable AI in regulated environments. I focus on delivering scalable, low-latency, and cost-efficient AI solutions that align with business goals and compliance requirements.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

AIML Engineer at Paypal

January 1, 2025 - Present

Led end-to-end LLM fine-tuning on 2M+ historical dispute cases using LoRA/SFT to auto-generate structured case summaries, reducing analyst handling time by 31% and improving summary quality (ROUGE-L +18%). Built a hybrid Retrieval-Augmented Generation pipeline (BM25 + dense embeddings) integrating Snowflake data, merchant metadata, and policy docs via vector search, reducing hallucinations by 40% and achieving 92% grounded response precision. Designed structured decision recommendations (liability, policy citation, risk signals) using function-calling and JSON schema validation, driving 85% analyst adoption in the initial release. Orchestrated scalable training/inference pipelines on AWS SageMaker with MLflow tracking and quantized inference, achieving sub-700ms p95 latency and 22% cost reduction. Implemented SHAP explanations and PII masking to meet compliance, improving model trust and reducing audit escalations by 35%. Established real-time data and embedding drift monitoring with a

Machine Learning Scientist at Persistent Systems

January 1, 2020 - August 1, 2023

Directed development of a real-time personalization platform for a Fortune 100 retailer, implementing collaborative ranking algorithms that increased click-through rate by 19% and boosted average order value by 11% in production. Deployed a GPU-accelerated computer vision pipeline for automated defect detection, reducing inspection time by 42% and achieving 96% detection accuracy in live environments. Established a scalable feature engineering and data processing framework using Apache Spark and SQL pipelines to process 30M–40M records daily across distributed production environments, reducing data preparation time by 35%. Designed transformer-based NLP workflows integrated with LangChain to orchestrate document retrieval and semantic search pipelines, improving intent classification accuracy by 25% and reducing manual triage effort. Crafted and trained deep learning models using PyTorch with custom training loops and hyperparameter tuning to improve model generalization and reduce o