Machine learning engineer

Achal Gandhi

**************@*****.*** +1-647-***-****

PROFILE

•3+ years of experience as a Machine Learning Engineer and Data Scientist with expertise in developing and deploying machine learning solutions for dynamic business applications.

•Proficient in building predictive models, natural language processing (NLP) systems, and real-time data analytics solutions.

•Strong programming skills in Python, R, Java, JavaScript, and MATLAB, with experience in frameworks such as TensorFlow, PyTorch, Scikit-learn, Flask, and FastAPI.

•Expertise in big data technologies like Spark, Hadoop, Kafka, and Hive for processing and managing large datasets efficiently.

•Skilled in cloud computing platforms including AWS (S3, EC2, Lambda, Redshift, SageMaker), Google Cloud

(BigQuery, Dataflow), and Azure for deployment and optimization.

•Advanced knowledge of database systems (SQL, MongoDB, Cassandra, PostgreSQL, Neo4j) and visualization tools (Tableau, Power BI, Matplotlib, Seaborn).

•Experienced in deploying RESTful APIs, integrating GraphQL, optimizing API performance, and managing API gateways for robust communication with ML models.

•Hands-on experience with CI/CD tools like Docker, Jenkins, Kubernetes, Terraform, and Apache Airflow for scalable and reliable deployment pipelines.

•Proficient in statistical modeling, time-series forecasting, classification, regression, clustering, anomaly detection, and advanced ensemble techniques.

•Strong skills in version control systems like Git and DVC for data and code management, ensuring reproducibility and collaboration.

•Passionate about solving real-world problems through data-driven insights, optimization algorithms, and cutting-edge AI solutions.

•Demonstrates efficiency in Structured Query Language (SQL) to access, retrieve, and manipulate data from databases on various platforms.

PROFESSIONAL EXPERIENCE

Machine learning engineer

SCALE AI

•Developed scalable machine learning models for predictive analytics using TensorFlow, PyTorch, and Scikit-learn.

01/2024 – present

•Built data preprocessing pipelines with Apache Airflow, automating feature engineering tasks to improve efficiency.

•Conducted data analysis using SQL to identify and resolve data quality issues, ensuring accuracy and integrity.

•Conducted hyperparameter tuning to enhance model performance, achieving a 15% increase in accuracy.

•Designed dashboards using Tableau and Power BI for real-time monitoring of machine learning model metrics.

•Utilized advanced machine learning and deep learning techniques to develop predictive analytics models, driving actionable insights.

•Deployed RESTful APIs to serve machine learning predictions, enabling real-time insights for stakeholders.

•Collaborated with cross-functional teams to ensure seamless deployment of ML workflows into production systems.

•Conducted model validation, performance benchmarking, and A/B testing to ensure robustness and reliability of predictions.

•Implemented feature selection techniques to improve computational efficiency and model accuracy.

•Integrated real-time data pipelines using Kafka and Spark for dynamic data processing.

•Provided technical mentorship to junior team members on advanced machine learning techniques.

•Deployed machine learning pipelines using Kubernetes and Docker Swarm for scalable and distributed model inference.

•Leveraged AWS SageMaker for model training and deployment, reducing infrastructure management overhead.

Data Engineer

Knorket AI

•Built and maintained data pipelines using Spark, Hadoop, and Hive for processing large datasets efficiently.

01/2023 – 12/2023

•Designed ETL workflows to process and transform structured and unstructured data.

•Developed backend algorithms integrating machine learning libraries like XGBoost, LightGBM, and CatBoost.

•Automated data processing tasks using Python and Apache Airflow, reducing manual workload by 20%.

•Conducted exploratory data analysis using Pandas, Matplotlib, and Seaborn to identify patterns and trends in datasets.

•Designed data models to optimize storage and retrieval efficiency in MongoDB, Cassandra, and Redshift.

•Deployed data workflows in AWS Lambda and Google Cloud Functions for scalable cloud processing.

•Collaborated with data scientists to implement advanced statistical models and evaluate their impact.

•Managed data versioning and integrity using Git, DVC, and Terraform.

•Developed monitoring scripts and integrated Grafana dashboards to ensure data pipeline reliability and uptime.

Data Scientist

Voiceflip Technology

•Conducted in-depth data analysis to support product decisions, leveraging Python, Pandas, and SQL.

02/2022 – 12/2022

•Designed and implemented machine learning models for customer segmentation, churn prediction, and behavior analysis.

•Built interactive dashboards using Power BI and Tableau to visualize key performance metrics for stakeholders.

•Deployed predictive models as REST APIs using Flask and FastAPI for seamless integration with existing systems.

•Automated data cleaning, transformation, and feature extraction tasks to improve efficiency.

•Conducted feature engineering to enhance model performance in classification and regression tasks.

•Performed hypothesis testing and statistical analysis to validate business assumptions and improve decision-making.

•Collaborated with cross-functional teams to define and prioritize data science projects aligned with business goals.

•Managed data ingestion pipelines to collect real-time data.

•Documented workflows, created training materials, and conducted knowledge- sharing sessions for team members.

•Used advanced clustering algorithms like DBSCAN and K-Means for unsupervised learning tasks.

SKILLS

Programming Language: Python, JAVA, R,JavaScript, MATLAB, C, C++, Android, Maple. Database: SQL, MongoDB, Cassandra, GraphSQL, PostgreSQL, Neo4j Technologies: Docker, Tableau, PowerBI, Django, Postman, Swagger, Redash, Excel, Docker, TensorFlow, PyTorch, Scikit-learn, Flask, FastAPI, Spark, Hive Cloud Technologies: AWS, Google Cloud Platform

Development Tools: Jenkins, Apache Airflow, Terraform, Git, DVC Core Competencies: Machine Learning, Data Engineering, API Integration, Statistical Analysis, Big Data Processing, Distributed Systems, Mathematical Modeling and Analysis, Automation Systems Design, Robotics Programming (ROS).

EDUCATION

Bachelor of Science Double Major in Computer Science and Applied Mathematics from York University, Toronto Awards: Math and Stats Department Award for Excellent Achievement Diploma in Automation and Robotics from Centennial college, Toronto Awards: Robotics Programming Award

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