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I am an AI Engineer with 3+ years of experience building machine learning and generative AI solutions from data preparation to production deployment. I design predictive models, deep learning workflows, and retrieval-augmented applications using fine-tuning, semantic search, and evaluation-driven iteration. I collaborate with product, engineering, and analytics teams to deliver secure, compliant, and high-availability AI systems that align with business goals.\n\nI work on scalable data and AI pipelines, model integration via APIs, and MLOps practices for versioning, monitoring, and governance. I am passionate about delivering robust AI solutions that unlock business value while maintaining privacy and regulatory compliance.…I am an AI Engineer with 3+ years of experience building machine learning and generative AI solutions from data preparation to production deployment. I design predictive models, deep learning workflows, and retrieval-augmented applications using fine-tuning, semantic search, and evaluation-driven iteration. I collaborate with product, engineering, and analytics teams to deliver secure, compliant, and high-availability AI systems that align with business goals.\n\nI work on scalable data and AI pipelines, model integration via APIs, and MLOps practices for versioning, monitoring, and governance. I am passionate about delivering robust AI solutions that unlock business value while maintaining privacy and regulatory compliance.

Mahendar Reddy Lakkireddy

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

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I am an AI Engineer with 3+ years of experience building machine learning and generative AI solutions from data preparation to production deployment. I design predictive models, deep learning workflows, and retrieval-augmented applications using fine-tuning, semantic search, and evaluation-driven iteration. I collaborate with product, engineering, and analytics teams to deliver secure, compliant, and high-availability AI systems that align with business goals.\n\nI work on scalable data and AI pipelines, model integration via APIs, and MLOps practices for versioning, monitoring, and governance. I am passionate about delivering robust AI solutions that unlock business value while maintaining privacy and regulatory compliance.…I am an AI Engineer with 3+ years of experience building machine learning and generative AI solutions from data preparation to production deployment. I design predictive models, deep learning workflows, and retrieval-augmented applications using fine-tuning, semantic search, and evaluation-driven iteration. I collaborate with product, engineering, and analytics teams to deliver secure, compliant, and high-availability AI systems that align with business goals.\n\nI work on scalable data and AI pipelines, model integration via APIs, and MLOps practices for versioning, monitoring, and governance. I am passionate about delivering robust AI solutions that unlock business value while maintaining privacy and regulatory compliance.

Available to hire

I am an AI Engineer with 3+ years of experience building machine learning and generative AI solutions from data preparation to production deployment. I design predictive models, deep learning workflows, and retrieval-augmented applications using fine-tuning, semantic search, and evaluation-driven iteration. I collaborate with product, engineering, and analytics teams to deliver secure, compliant, and high-availability AI systems that align with business goals.\n\nI work on scalable data and AI pipelines, model integration via APIs, and MLOps practices for versioning, monitoring, and governance. I am passionate about delivering robust AI solutions that unlock business value while maintaining privacy and regulatory compliance.

Skills

Experience Level

Expert

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Work Experience

AI Engineer at Clairvoyant

March 1, 2025 - Present

Designed end-to-end retrieval-augmented generation (RAG) system using Pinecone vector DB with fine-tuned BERT and RoBERTa models for natural language search over 500K+ policy documents; reduced retrieval time via transformer-encoded embeddings at scale. Optimized domain embeddings on proprietary insurance corpora with Hugging Face and LoRA, achieving 44% higher retrieval accuracy on compliance-critical queries while maintaining efficiency. Built transformer-based NER systems in PyTorch to extract obligations, exclusions, and risk indicators across 15 jurisdictions for governance and regulatory compliance. Implemented ingestion pipelines with Airflow and Kafka for continuous policy-doc streams and automated regulatory data lineage tracking for audits. Configured MongoDB clusters for audit-compliant storage; managed dual vector stores (Chroma, FAISS) to achieve sub-second semantic search latency on million-scale embeddings with hybrid retrieval. Developed agentic workflows using GPT-4 an

Junior Data Scientist at TCS

May 1, 2021 - December 1, 2023

Engineered neural ranking system using PyTorch to match 20K+ daily driver-rider requests, achieving 35% improvement in match quality with sub-100ms inference. Built real-time streaming pipelines with Apache Spark and Kafka across 50+ cities, enabling dynamic feature generation for online ranking. Developed multi-step-ahead demand forecasting across 50+ cities to predict surges and driver availability, reducing wait times by 31% during peak hours. Applied SHAP and LIME to explain predictions and inform product decisions; performed exploratory analysis with Tableau/Power BI. Created feature pipelines with Pandas/NumPy for personalized rankings and deployed containerized models on AWS SageMaker/Lambda with CI/CD via Jenkins and MLflow for versioning and drift detection, supporting millions of inferences.