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AI/ML Engineer with 4+ years of experience designing, developing, and deploying production-scale machine learning and generative AI systems. Specialized in LLMs, RAG, RLHF, NLP, deep learning, model evaluation, and AI alignment. Hands-on experience building LLM evaluation platforms, vector search/retrieval systems, automated benchmarking frameworks, and scalable MLOps pipelines. Proven ability to improve model quality and reliability through data-driven experimentation, monitoring, and continuous iteration for enterprise-scale deployments.…AI/ML Engineer with 4+ years of experience designing, developing, and deploying production-scale machine learning and generative AI systems. Specialized in LLMs, RAG, RLHF, NLP, deep learning, model evaluation, and AI alignment. Hands-on experience building LLM evaluation platforms, vector search/retrieval systems, automated benchmarking frameworks, and scalable MLOps pipelines. Proven ability to improve model quality and reliability through data-driven experimentation, monitoring, and continuous iteration for enterprise-scale deployments.

Chandra Bhuvan

AI Engineer, Data Scientist, Programmer, +4

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AI/ML Engineer with 4+ years of experience designing, developing, and deploying production-scale machine learning and generative AI systems. Specialized in LLMs, RAG, RLHF, NLP, deep learning, model evaluation, and AI alignment. Hands-on experience building LLM evaluation platforms, vector search/retrieval systems, automated benchmarking frameworks, and scalable MLOps pipelines. Proven ability to improve model quality and reliability through data-driven experimentation, monitoring, and continuous iteration for enterprise-scale deployments.…AI/ML Engineer with 4+ years of experience designing, developing, and deploying production-scale machine learning and generative AI systems. Specialized in LLMs, RAG, RLHF, NLP, deep learning, model evaluation, and AI alignment. Hands-on experience building LLM evaluation platforms, vector search/retrieval systems, automated benchmarking frameworks, and scalable MLOps pipelines. Proven ability to improve model quality and reliability through data-driven experimentation, monitoring, and continuous iteration for enterprise-scale deployments.

Available to hire

AI/ML Engineer with 4+ years of experience designing, developing, and deploying production-scale machine learning and generative AI systems. Specialized in LLMs, RAG, RLHF, NLP, deep learning, model evaluation, and AI alignment. Hands-on experience building LLM evaluation platforms, vector search/retrieval systems, automated benchmarking frameworks, and scalable MLOps pipelines.

Proven ability to improve model quality and reliability through data-driven experimentation, monitoring, and continuous iteration for enterprise-scale deployments.

Skills

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Intermediate

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Language

Work Experience

AI/ML Engineer at Scale AI

April 1, 2025 - Present

Designed and deployed large-scale LLM evaluation frameworks to benchmark GPT, Claude, Gemini, and Llama models across reasoning, factuality, safety, and instruction-following tasks using 10K+ prompts weekly. Built automated evaluation pipelines using Python, FastAPI, Pandas, LangChain, and OpenAI APIs, reducing manual review effort by 65% and accelerating assessment cycles by 3x. Developed RAG-based evaluation frameworks for enterprise knowledge assistants to measure retrieval accuracy, answer grounding, and hallucination rates across millions of documents. Engineered scalable retrieval pipelines with LangChain, embedding models, and vector databases (FAISS/Pinecone), improving context relevance and reducing unsupported responses. Implemented automated scoring using LLM-as-a-Judge with rule-based metrics and human signals; created hallucination detection and quality validation systems. Built AWS-based data pipelines processing 5M+ evaluation records and integrated human-in-the-loop fee

Machine Learning Engineer at Accenture

January 1, 2021 - December 1, 2023

Developed and deployed machine learning solutions for enterprise clients across fraud detection, customer churn prediction, risk scoring, and customer support analytics. Trained supervised models using Scikit-Learn, XGBoost, and ensemble techniques; performed EDA, statistical analysis, feature selection, and KPI-driven performance evaluation. Built automated ML evaluation pipelines tracking accuracy, precision, recall, F1-score, ROC-AUC, and business KPIs for 100+ production models. Conducted model validation and cross-validation with MLflow experiment tracking to ensure reproducibility. Implemented monitoring for data drift, concept drift, prediction quality, and anomaly detection, reducing detection time by 70%. Added data quality validation and integrity checks, reducing downstream prediction errors by 30%. Built NLP solutions for ticket classification, intent detection, and sentiment analysis on 2M+ interactions. Created and maintained large-scale annotated datasets; built retraini