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AI/ML Engineer with 3+ years of experience building and deploying scalable Machine Learning, Deep Learning, and Generative AI solutions in production environments. Expertise in LLM-based applications including RAG systems, agentic AI workflows, and multi-agent orchestration, with a strong foundation in NLP, embeddings, and semantic search. Proven ability to develop end-to-end ML systems covering churn prediction, anomaly detection, and recommendation systems using modern ML frameworks and feature engineering techniques. Proficient in taking models from development to production using MLOps practices, cloud platforms, and efficient data pipelines.…AI/ML Engineer with 3+ years of experience building and deploying scalable Machine Learning, Deep Learning, and Generative AI solutions in production environments. Expertise in LLM-based applications including RAG systems, agentic AI workflows, and multi-agent orchestration, with a strong foundation in NLP, embeddings, and semantic search. Proven ability to develop end-to-end ML systems covering churn prediction, anomaly detection, and recommendation systems using modern ML frameworks and feature engineering techniques. Proficient in taking models from development to production using MLOps practices, cloud platforms, and efficient data pipelines.

R Sreech eek uri T X

AI Engineer, Data Scientist, Software Engineer, +1

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AI/ML Engineer with 3+ years of experience building and deploying scalable Machine Learning, Deep Learning, and Generative AI solutions in production environments. Expertise in LLM-based applications including RAG systems, agentic AI workflows, and multi-agent orchestration, with a strong foundation in NLP, embeddings, and semantic search. Proven ability to develop end-to-end ML systems covering churn prediction, anomaly detection, and recommendation systems using modern ML frameworks and feature engineering techniques. Proficient in taking models from development to production using MLOps practices, cloud platforms, and efficient data pipelines.…AI/ML Engineer with 3+ years of experience building and deploying scalable Machine Learning, Deep Learning, and Generative AI solutions in production environments. Expertise in LLM-based applications including RAG systems, agentic AI workflows, and multi-agent orchestration, with a strong foundation in NLP, embeddings, and semantic search. Proven ability to develop end-to-end ML systems covering churn prediction, anomaly detection, and recommendation systems using modern ML frameworks and feature engineering techniques. Proficient in taking models from development to production using MLOps practices, cloud platforms, and efficient data pipelines.

Available to hire

AI/ML Engineer with 3+ years of experience building and deploying scalable Machine Learning, Deep Learning, and Generative AI solutions in production environments. Expertise in LLM-based applications including RAG systems, agentic AI workflows, and multi-agent orchestration, with a strong foundation in NLP, embeddings, and semantic search.

Proven ability to develop end-to-end ML systems covering churn prediction, anomaly detection, and recommendation systems using modern ML frameworks and feature engineering techniques. Proficient in taking models from development to production using MLOps practices, cloud platforms, and efficient data pipelines.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

AI/ML Engineer at Q2 Holdings

June 1, 2025 - Present

Built GenAI-powered search and Q&A systems using LLMs (GPT-4, Claude), reducing customer query resolution time by 30–40% and saving 200+ support hours/month. Engineered scalable RAG pipelines using LangChain and LlamaIndex with FAISS/Weaviate vector databases and evaluation metrics to improve answer relevance by ~35%. Reduced hallucinations by 30% via prompt engineering, re-ranking strategies, and structured evaluation pipelines using RAGAS faithfulness scores. Optimized LLM performance and cost using Redis caching and request batching to reduce inference latency and compute costs. Deployed cloud-native AI solutions on AWS (S3, EC2, EKS) using containerized microservices and scalable inference endpoints with high availability and auto-scaling. Improved model response accuracy by 20–25% through fine-tuning/evaluation with PyTorch/TensorFlow using LoRA/QLoRA. Trained churn prediction models (LightGBM, XGBoost, Random Forest) with behavioral/transactional features. Established model m

ML Engineer at Accenture, India

May 1, 2022 - July 31, 2024

Architected end-to-end data pipelines with PySpark and SQL processing 40–50M records/day across retail and insurance datasets, reducing data processing time by 25–30% and enabling reliable data availability for ML training/inference. Developed and tuned ML models for churn (CatBoost, LightGBM, Logistic Regression) and ARIMA models for sales forecasting across 5M+ customers, achieving AUC ~0.84–0.88 and improving prediction accuracy by 15–18%, with churn reduction of 10–12%. Performed advanced feature engineering on behavioral and time-series data using pandas/NumPy, improving performance by 12–15%. Designed recommendation systems using SVD and collaborative filtering, improving campaign conversion rate by 8–10%. Implemented anomaly detection models (Isolation Forest, Autoencoders) for irregular sales patterns, pricing anomalies, and suspicious claim activity, reducing revenue leakage by 15%. Served models as REST APIs using FastAPI/Flask, containerized with Docker and orc