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I am an AI/ML Engineer with 3+ years of experience building production AI systems across retail, healthcare, and telecom. I specialize in developing enterprise GenAI platforms and RAG pipelines on Databricks, with a strong focus on evaluation, observability, and responsible deployment. I thrive on turning complex data into scalable AI products that drive measurable business impact. My background includes LLM fine-tuning, predictive maintenance, anomaly detection, and deploying classical ML solutions on cloud platforms. I enjoy crafting end-to-end AI solutions that are robust, auditable, and user-friendly, delivering tangible value for diverse stakeholders.…I am an AI/ML Engineer with 3+ years of experience building production AI systems across retail, healthcare, and telecom. I specialize in developing enterprise GenAI platforms and RAG pipelines on Databricks, with a strong focus on evaluation, observability, and responsible deployment. I thrive on turning complex data into scalable AI products that drive measurable business impact. My background includes LLM fine-tuning, predictive maintenance, anomaly detection, and deploying classical ML solutions on cloud platforms. I enjoy crafting end-to-end AI solutions that are robust, auditable, and user-friendly, delivering tangible value for diverse stakeholders.

Chaitrali K Atkar

AI Engineer, Data Scientist, Developer, +2

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I am an AI/ML Engineer with 3+ years of experience building production AI systems across retail, healthcare, and telecom. I specialize in developing enterprise GenAI platforms and RAG pipelines on Databricks, with a strong focus on evaluation, observability, and responsible deployment. I thrive on turning complex data into scalable AI products that drive measurable business impact. My background includes LLM fine-tuning, predictive maintenance, anomaly detection, and deploying classical ML solutions on cloud platforms. I enjoy crafting end-to-end AI solutions that are robust, auditable, and user-friendly, delivering tangible value for diverse stakeholders.…I am an AI/ML Engineer with 3+ years of experience building production AI systems across retail, healthcare, and telecom. I specialize in developing enterprise GenAI platforms and RAG pipelines on Databricks, with a strong focus on evaluation, observability, and responsible deployment. I thrive on turning complex data into scalable AI products that drive measurable business impact. My background includes LLM fine-tuning, predictive maintenance, anomaly detection, and deploying classical ML solutions on cloud platforms. I enjoy crafting end-to-end AI solutions that are robust, auditable, and user-friendly, delivering tangible value for diverse stakeholders.

Available to hire

I am an AI/ML Engineer with 3+ years of experience building production AI systems across retail, healthcare, and telecom. I specialize in developing enterprise GenAI platforms and RAG pipelines on Databricks, with a strong focus on evaluation, observability, and responsible deployment. I thrive on turning complex data into scalable AI products that drive measurable business impact.

My background includes LLM fine-tuning, predictive maintenance, anomaly detection, and deploying classical ML solutions on cloud platforms. I enjoy crafting end-to-end AI solutions that are robust, auditable, and user-friendly, delivering tangible value for diverse stakeholders.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Databricks

July 1, 2025 - Present

Built a multi-agent GenAI platform for campaign ideation, content generation, knowledge retrieval using Agent Bricks, Vector Search, Model Serving, MCP integrations, reducing campaign-planning turnaround by 75% for a retail enterprise (13,000+ locations). Ingested and structured enterprise knowledge into a unified, governed knowledge repository using Delta Lake and Unity Catalog. Improved retrieval precision by 25% by designing RAG pipelines with hybrid search, reranking, and metadata filtering across fragmented enterprise knowledge sources. Developed LLM evaluation framework to measure factual grounding, retrieval effectiveness, and hallucination rates, improving answer quality by 18%. Implemented observability pipelines using MLflow to track model performance, prompt quality, latency, and retrieval metrics, cutting production-incident diagnosis time by 40%. Established deployment gates, drift monitoring, prompt versioning, and role-based approval workflows to ensure governed and reli

AI Engineer Intern at DeepThink Health

January 1, 2025 - June 1, 2025

Accomplished 91% document classification accuracy by fine-tuning Llama 3, Mistral on 100K+ specifications using LoRA and QLoRA. Improved RAG reliability from 82% to 89% by building grounded workflows with LangChain, Pinecone, ChromaDB. Reduced LLM misclassification by 60% by optimizing prompts with DSPy, A/B testing, W&B tracking, and expert validation. Strengthened model reliability by improving F1 through PyTorch-based evaluation workflows and human-in-the-loop feedback. Eliminated 12% disparity across manufacturers conducting fairness analysis, implementing balanced sampling for equitable outcomes.

AI Engineer at Jio Platforms Limited

July 1, 2022 - July 1, 2023

Developed AI-powered predictive maintenance system detecting network anomalies, reducing telecom site failures by 20%. Engineered 20+ time-series features from 1M+ telecom records covering latency, packet loss, and alarm frequency. Built ensemble models with Isolation Forest, TensorFlow Autoencoders, and XGBoost, achieving 85% failure prediction accuracy. Designed site-level risk scoring framework classifying sites into Low/Medium/High/Critical tiers, improving prioritization by 30%. Deployed FastAPI inference on Docker, Kubernetes, Azure with sub-300ms latency; Grafana dashboards cut reporting effort by 40%.

Data Science Intern at The Sparks Foundation

March 1, 2021 - August 1, 2021

Spotify Music Recommender System project: Achieved 85% cross-validated accuracy by building a mood classification ML pipeline on Spotify audio features. Developed mood-based and KNN-based recommendation engines for personalized song suggestions. Designed a user-friendly Streamlit interface and deployed the containerized app on AWS EC2 using Docker. Enhanced model interpretability with EDA and correlation analysis across 10,000+ Spotify tracks to surface key mood patterns. Conducted data preprocessing, feature engineering, model tuning (Logistic Regression, SVM, Random Forest), and version control with GitHub.