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Aravind B

# AI/ML Engineer

Actively looking · Member since 17 Feb 2025 [Message](https://jobicy.com/dashboard-page.md)

ShareLocationUnited StatesDesired salaryUnspecifiedWork preferenceContractExperience levelNot set
* [Overview](#overview)
* [Portfolio 0](#portfolio)
* [Services 0](#services)

## About

Professional summary•	AI/ML Engineer with 9+ years of expertise in Data Science, Machine Learning, Deep Learning, and Large Language Models (LLMs), specializing in building, fine-tuning, and deploying AI-driven solutions at scale.
•	Proficient in Python and experienced in end-to-end ML workflows, including data collection, preprocessing, EDA, model training, and deployment using cloud and MLOps practices.
•	Expertise in Supervised and Unsupervised Learning algorithms for classification, regression, clustering, and anomaly detection.
•	Designed machine learning algorithms such as XGBoost, Random Forest, Support Vector Machines (SVM), Logistic Regression, and K-Means, implementing techniques like outlier detection, hyperparameter tuning, cross-validation, and feature engineering, resulting in up to a 15% improvement in model accuracy.
•	Hands-on experience with Deep Learning frameworks such as TensorFlow, PyTorch, and Keras, implementing CNNs, RNNs, LSTMs, GANs, and transformer-based NLP models for various AI applications.
•	Fine-tuned and deployed LLMs such as GPT, BERT, LLaMA, and Falcon for NLP applications like text generation, summarization, and Q&A.
•	Implemented Retrieval-Augmented Generation (RAG) architectures by integrating LLMs with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone) to enhance search and response accuracy.
•	Optimized LLM inference using quantization techniques (GPTQ, LoRA, DeepSpeed, and Hugging Face Transformers) to reduce latency and improve efficiency.
•	Processed large-scale data using HDFS, MapReduce, and PySpark, optimizing model training for high-performance and distributed ML workloads.
•	Designed and implemented scalable ML pipelines leveraging big data technologies to handle structured and unstructured datasets.
•	Built and deployed AI models using FastAPI, Flask, and Streamlit, ensuring real-time inference and seamless integration with applications.
•	Containerized AI models with Docker and Kubernetes, deploying them on cloud-based infrastructures for scalability and high availability.
•	Developed serverless and microservices-based ML architectures for efficient model inference and API exposure.
•	Worked with AWS, GCP, and Azure, utilizing AI/ML services such as SageMaker, Bedrock, Vertex AI, AutoML, Azure ML & AI, EC2, Lambda, BigQuery, and S3 for scalable model training and deployment.
•	Designed CI/CD pipelines using GitHub Actions, MLflow for model tracking, and Terraform for cloud infrastructure automation.
•	Implemented monitoring, logging, and performance tracking using AWS CloudWatch, GCP Cloud Monitoring, and Azure Monitor for observability.
•	Integrated security and audit logging using AWS CloudTrail logs to track model performance, API access, and system events in production.
•	Implemented monitoring, logging, and performance tracking with Prometheus, Grafana, and ELK stack for AI models in production.
•	Deployed containerized workloads on Kubernetes clusters (EKS, GKE, AKS), managing cloud-based MLOps pipelines for automated retraining and deployment.
•	Proficient in evaluation metrics such as precision, recall, F1-score, and AUC-ROC, ensuring 95%+ accuracy and 90% precision in classification and regression tasks.

## Skills

7 capabilities[Amazon Web Services (AWS)](https://jobicy.com/talent/amazon-web-services-aws.md)[Azure](https://jobicy.com/talent/azure.md)[GCP](https://jobicy.com/talent/gcp.md)[Python](https://jobicy.com/talent/python.md)[SQL](https://jobicy.com/talent/sql.md)[TensorFlow](https://jobicy.com/talent/tensorflow.md)[Terraform](https://jobicy.com/talent/terraform.md)

## Experience

Career historyNo experience data available.

## Education

Learning historyNo education data available.

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