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I am an AI/ML Engineer with 4+ years of experience in MLOps, DevOps, and Generative AI. I design and deploy NLP, GenAI, and recommendation systems, including automated scoring, chatbots, RAG pipelines, personalization, and ranking models. I specialize in LLM optimization (LoRA, QLoRA, PEFT, RLHF) and traditional NLP techniques for short-answer and essay evaluation, text classification, embeddings, and search relevance. I have delivered AI-driven automation across banking, healthcare, and telecom, achieving measurable business impact. I am pursuing a Master’s in Information Science (Machine Learning) at the University of Arizona, focusing on reinforcement learning, graph learning, and generative AI. I value bias mitigation, drift detection, explainability, and monitoring to ensure fairness and transparency in deployed models. I excel in scalable ML infrastructure and distributed training (Spark, Ray, GPU/TPU) for real-time, high-volume systems. I am passionate about applying NLP and ML to learning, assessment, and personalized education, turning complex models into practical solutions that empower users and learners.…I am an AI/ML Engineer with 4+ years of experience in MLOps, DevOps, and Generative AI. I design and deploy NLP, GenAI, and recommendation systems, including automated scoring, chatbots, RAG pipelines, personalization, and ranking models. I specialize in LLM optimization (LoRA, QLoRA, PEFT, RLHF) and traditional NLP techniques for short-answer and essay evaluation, text classification, embeddings, and search relevance. I have delivered AI-driven automation across banking, healthcare, and telecom, achieving measurable business impact. I am pursuing a Master’s in Information Science (Machine Learning) at the University of Arizona, focusing on reinforcement learning, graph learning, and generative AI. I value bias mitigation, drift detection, explainability, and monitoring to ensure fairness and transparency in deployed models. I excel in scalable ML infrastructure and distributed training (Spark, Ray, GPU/TPU) for real-time, high-volume systems. I am passionate about applying NLP and ML to learning, assessment, and personalized education, turning complex models into practical solutions that empower users and learners.

Lakshmi Lahari Satti

AI Engineer, Data Scientist, Full Stack Developer, +2

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I am an AI/ML Engineer with 4+ years of experience in MLOps, DevOps, and Generative AI. I design and deploy NLP, GenAI, and recommendation systems, including automated scoring, chatbots, RAG pipelines, personalization, and ranking models. I specialize in LLM optimization (LoRA, QLoRA, PEFT, RLHF) and traditional NLP techniques for short-answer and essay evaluation, text classification, embeddings, and search relevance. I have delivered AI-driven automation across banking, healthcare, and telecom, achieving measurable business impact. I am pursuing a Master’s in Information Science (Machine Learning) at the University of Arizona, focusing on reinforcement learning, graph learning, and generative AI. I value bias mitigation, drift detection, explainability, and monitoring to ensure fairness and transparency in deployed models. I excel in scalable ML infrastructure and distributed training (Spark, Ray, GPU/TPU) for real-time, high-volume systems. I am passionate about applying NLP and ML to learning, assessment, and personalized education, turning complex models into practical solutions that empower users and learners.…I am an AI/ML Engineer with 4+ years of experience in MLOps, DevOps, and Generative AI. I design and deploy NLP, GenAI, and recommendation systems, including automated scoring, chatbots, RAG pipelines, personalization, and ranking models. I specialize in LLM optimization (LoRA, QLoRA, PEFT, RLHF) and traditional NLP techniques for short-answer and essay evaluation, text classification, embeddings, and search relevance. I have delivered AI-driven automation across banking, healthcare, and telecom, achieving measurable business impact. I am pursuing a Master’s in Information Science (Machine Learning) at the University of Arizona, focusing on reinforcement learning, graph learning, and generative AI. I value bias mitigation, drift detection, explainability, and monitoring to ensure fairness and transparency in deployed models. I excel in scalable ML infrastructure and distributed training (Spark, Ray, GPU/TPU) for real-time, high-volume systems. I am passionate about applying NLP and ML to learning, assessment, and personalized education, turning complex models into practical solutions that empower users and learners.

Available to hire

I am an AI/ML Engineer with 4+ years of experience in MLOps, DevOps, and Generative AI. I design and deploy NLP, GenAI, and recommendation systems, including automated scoring, chatbots, RAG pipelines, personalization, and ranking models. I specialize in LLM optimization (LoRA, QLoRA, PEFT, RLHF) and traditional NLP techniques for short-answer and essay evaluation, text classification, embeddings, and search relevance. I have delivered AI-driven automation across banking, healthcare, and telecom, achieving measurable business impact. I am pursuing a Master’s in Information Science (Machine Learning) at the University of Arizona, focusing on reinforcement learning, graph learning, and generative AI. I value bias mitigation, drift detection, explainability, and monitoring to ensure fairness and transparency in deployed models. I excel in scalable ML infrastructure and distributed training (Spark, Ray, GPU/TPU) for real-time, high-volume systems.

I am passionate about applying NLP and ML to learning, assessment, and personalized education, turning complex models into practical solutions that empower users and learners.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Machine Learning Engineer at Accenture

January 31, 2023 - January 31, 2023

Led AI/ML initiatives across multiple clients. Biogen ComBi project: built predictive ML models for clinical trial logistics, developed time-series forecasting pipelines to improve trial operations, automated data ingestion for large-scale datasets, applied OCR+NLP to extract insights from trial documents, and designed drift detection and retraining workflows to sustain model accuracy and fairness. Conducted A/B testing and statistical validation, and implemented ranking/prioritization methods to streamline trial data evaluation, reducing manual review time by ~30%.

Machine Learning Engineer at Accenture

December 31, 2023 - December 31, 2023

CIBC EC RM Integration: designed real-time risk scoring and anomaly detection models to strengthen fraud prevention; applied NLP to unstructured data (emails, chat logs) to broaden fraud detection coverage; built customer behavior & personalization models improving retention; conducted multivariate testing to optimize thresholds and KPI impact; delivered explainability and compliance reports for auditors; built feature engineering pipelines and deployed low-latency APIs for production-grade fraud systems.

Machine Learning Engineer at Accenture

December 31, 2024 - December 31, 2024

AT&T: engineered real-time anomaly detection for telecom networks; applied deep learning for content understanding and contextual query matching to improve knowledge base search relevance; deployed LLM-powered retrieval assistants reducing troubleshooting time; implemented LLM-based customer service assistants; designed ranking-based personalization for knowledge retrieval; built unsupervised pipelines for large-scale log analysis; developed predictive maintenance models; explored reinforcement learning for adaptive anomaly detection and created simulation frameworks for stress-testing; built cloud-native MLOps pipelines with sub-second latency for millions of daily requests.