Home

Aarush Bhardwaj - Senior Machine Learning Engineer

[email protected]

Location: New York City, New York, USA

Relocation: yes

Visa: OPT EAD

Name: Aarush Bhardwaj Email: [email protected] Contact: +1 330-808-6778 LinkedIn: linkedin.com/in/aarushbhardwaj Senior Machine Learning Engineer SUMMARY As a seasoned Senior Machine Learning, Data Scientist, and MLOps Engineer with over 7 years of hands-on experience in designing, developing, and deploying cutting-edge machine learning solutions across diverse industries, I bring profound expertise in driving AI initiatives from conceptualization to execution. My leadership is characterized by a strategic vision for leveraging AI to solve complex business challenges, foster innovation, and enhance operational efficiencies. With a proven track record in building and mentoring high-performing teams, I excel in creating collaborative environments that inspire innovation and continuous learning. My comprehensive background encompasses the entire machine learning lifecycle, including data engineering, model development, MLOps, and AI strategy formulation, making me adept at leading transformative AI projects that deliver tangible business value. I am passionate about harnessing the power of AI to drive digital transformation, optimize decision-making processes, and create competitive advantages TECHNICAL SKILLS Methodology SDLC, Agile and Scrum, Waterfall Programming Languages & Frameworks Python, Django, Flask, R, SQL, Java, Scala, TensorFlow, PyTorch, Keras, GPT-3, GPT-4, Palm AI. Machine Learning & AI Deep Learning, Recommender Systems, NLP, Computer Vision, LLMs (Large Language Models), AutoML, Artificial Neural Networks Database MySQL, PostgreSQL, SQL Server, MongoDB, Snowflake, Spark SQL Data Engineering & MLOps Apache Spark, Databricks, ML Ops tools (ML flow, Kubeflow), Terraform, Kubernetes, Azure Machine Learning, AWS Sagemaker. Cloud Technologies & Databases AWS, GCP, Azure, MySQL, PostgreSQL, MongoDB, Snowflake, Big Query Packages ggplot2, Pandas, NumPy, Matplotlib, Scikit-Learn, SciPy, PyTorch, TensorFlow, Keras, Docker, Apache Spark, Apache Airflow, Apache Kafka, MLlib, Data Bricks, Selenium Testing Jenkins, Pytest Other Tools & Technologies Docker, Jenkins, Pytest, Apache Kafka, Git, MS Office Operating Systems Windows, MacOS, Linux EDUCATION Bachelor of Computer Applications at Punjab Technical University June 2017 Master of Science in Business Analytics at Kent State University May 2022 CERTIFICATION Machine Learning Specialization from Coursera MLOps Specialization from Coursera PROFESSIONAL EXPERIENCE Acadia Healthcare, New York, NY Oct 2023 Till Date Senior Machine Learning Engineer Responsibilities: Led Cross-Functional ML Projects: Spearheaded a cross-functional team to implement a churn prediction model using Google's Large Language Model Palm AI, achieving a 60% reduction in employee churn and saving $2 million annually in recruitment and training costs. Innovative LLMOps Framework: Designed and deployed a comprehensive LLMOps framework, reducing model update cycles by 40%, which facilitated rapid and scalable deployment of AI models across the organization. Team Management & Productivity: Directly managed a team of 15 engineers, fostering a collaborative environment that resulted in a 30% increase in productivity and improved project delivery timelines by 25%. Operational Efficiency Through MLOps: Implemented MLOps practices that automated the ML lifecycle, leading to a 50% reduction in time-to-market for new AI features and models, and a 25% increase in operational efficiency. Strategic Technology Leadership: Developed and executed a technology strategy that established Acadia Healthcare as a leader in AI applications within the healthcare sector, significantly enhancing its competitive position. Technology Stack: Python, XGBoost, Random Forest, LLM GPT-3, GPT-4, Palm AI, Azure Data Lake Storage, Azure Databricks, Vector Databases (e.g., Pinecone, Elasticsearch), RAG, SQL, Pyspark, Spark ML, DevOps, LLMOps. Deckers Brands, New York, NY Aug 2022 Oct 2023 Senior Machine Learning Engineer Responsibilities: Product Recommendation System Leadership: Led developing and deploying a product recommendation system using collaborative filtering and deep learning, resulting in a 42% increase in sales and a $4 million boost in quarterly revenue. Sentiment Analysis Project: Directed a sentiment analysis initiative using Transformer-based architectures, achieving a 90% accuracy rate, enhancing customer service responsiveness by 45%, and increasing customer satisfaction scores by 15% year-over-year. MLOps Strategy for Retail Analytics: Spearheaded the creation of an MLOps strategy, streamlining model deployment and scaling across the retail analytics platform, leading to a 35% improvement in model accuracy and a 40% reduction in operational bottlenecks. Team Leadership and Mentorship: Managed a team of 10 ML engineers, improving team efficiency by 20% and reducing recommendation errors by 57% through targeted mentoring and skill development initiatives. Operational Innovation: Pioneered an automated product image classification system that reduced manual workload by 80%, demonstrating the ability to lead high-impact projects that improve operational efficiency and reduce costs. Technology Stack: Python, TensorFlow, PyTorch, sci-kit-learn, Vector Databases, RAG, LLMOps practices for continuous deployment and monitoring of LLMs, ensuring high availability and performance Kent State University January 2022 Aug 2022 Senior Data Scientist/ Machine Learning Engineer Responsibilities: Automated Essay Feedback System: Developed an NLP system reducing grading time by 50%. Implemented RAG for enhanced feedback suggestions, employing advanced NLP techniques. Facial Recognition for Attendance: Achieved a 99.7% accuracy rate in facial recognition for attendance, significantly reducing administrative workload by 95% and optimizing serverless computing for cost savings. Fraud Detection System: Reduced fraudulent transactions by $1.2 million annually with a 98% detection rate, automating model deployment and monitoring to minimize financial losses. Impact: The automated essay feedback system saved over 1,000 faculty hours per semester by halving grading time, allowing educators to focus more on student engagement and less on administrative tasks. The facial recognition attendance system reduced administrative workload by 95%, saving the university an estimated $500K annually in operational costs. Technology Stack: Python, TensorFlow, Keras, PyTorch, Apache Spark, Hadoop, Vector Databases, RAG, LLMOps for efficient model management and deployment. Ault Care Insurance, Canton, OH Aug 2021 Dec 2021 Machine Learning Engineer Responsibilities: Fraud Detection Initiative: Led a project that achieved a 98% detection rate, reducing fraudulent transactions by $1.2M annually through predictive modeling and anomaly detection. Conversational AI for Customer Support: Developed a chatbot reducing response times by 60%, leveraging NLP for accurate intent recognition. Damage Assessment from Images: Created a computer vision system for damage assessment, achieving 94% accuracy and reducing manual inspection time by 80%, with an end-to-end MLOps pipeline. Impact: The fraud detection initiative prevented $1.2M in losses annually, significantly reducing the incidence of fraudulent claims by 98%. The deployment of a conversational AI for customer support improved customer satisfaction rates by over 20% and decreased operational costs by 15% through the reduction of response times. Technology Stack: TensorFlow, Keras, PyTorch, sci-kit-learn, Vector Databases, RAG, LLMOps practices for NLP models, ensuring robust performance and adaptability. Pepsi Co, India Jul 2017 Jan 2021 Machine Learning Engineer Responsibilities: Demand Forecasting: Directed a project that achieved a 90% accuracy rate with time series models, reducing excess inventory costs by 15%. Supply Chain Text Analysis: Implemented NLP models to extract insights from unstructured text, reducing decision-making time by 40% and achieving 20% cost savings in vendor selection. Quality Inspection: Developed a computer vision system for automated quality inspection, achieving a 96% accuracy rate in defect classification and enhancing product quality by reducing defects by 20%. Impact: The demand forecasting project led to a 15% reduction in excess inventory costs, translating to $3M in annual savings. The supply chain text analysis tools streamlined vendor selection processes, contributing to a 20% reduction in procurement costs, saving the company over $2M annually. Technology Stack: Python 3.X, TensorFlow, Keras, NumPy, Spark 2.0, Vector Databases, RAG, LLMOps for operational excellence, ensuring models' adaptability and reliability Keywords: artificial intelligence machine learning access management rlang microsoft Colorado New York Ohio