Design and development of supervised and unsupervised machine learning models for classification, regression, and clustering tasks.

Expert in model evaluation and optimization techniques such as cross-validation, grid search, and hyperparameter tuning.

Experience with key ML frameworks and libraries including TensorFlow, and Scikit-learn.

Deploy machine learning models into production using REST APIs, Flask, FastAPI, or cloud platforms.

Build end-to-end ML pipelines integrating data preprocessing, feature engineering, model training, and deployment.

Skilled in real-time and batch inference techniques for scalable machine learning applications.

Expertise in data wrangling, cleaning, and transformation to prepare datasets for analysis and modeling.

Hands-on experience in building machine learning models using Python, scikit-learn and TensorFlow

Strong knowledge of statistical analysis, data visualization, and predictive analytics.

Ability to design and maintain ETL pipelines for processing structured and unstructured data.

Experience working with large datasets and databases such as SQL, NoSQL, and cloud data warehouses (e.g., BigQuery, Redshift).

Proficient in data storytelling using tools like Power BI, Tableau, and Matplotlib.

Design and build robust ETL pipelines to ingest, transform, and store data efficiently.

Ensure data reliability with quality checks, schema validation, and governance practices.

Optimize storage and compute for cost efficiency and high-performance workflows.

Maintain version control and CI/CD for data infrastructure using Git and automation tools.

Deployed scalable data science applications using AWS services like EC2, S3, Lambda, and SageMaker for model training and hosting.

Built automated data pipelines using AWS Glue, Step Functions, and CloudWatch for orchestrating ETL jobs and ML workflows.

Integrated GCP services like BigQuery and Vertex AI for real-time analytics and large-scale model training and deployment.

Implemented model versioning, endpoint monitoring, and CI/CD pipelines for continuous delivery of ML services on cloud platforms.

Optimized cloud resources for cost-efficiency while maintaining high availability and performance of data science workloads.

Clean, explore, and visualize data to uncover patterns and insights using Python, R, and SQL.

Perform statistical analysis—hypothesis testing, A/B analysis, correlation, and regression models.

Develop interactive dashboards and reports with Tableau

Translate findings into clear data stories and actionable business recommendations.

Automate recurring analysis tasks and build reproducible analysis pipelines.

Collaborate with stakeholders to define KPIs, metrics, and data-driven business strategies.

Design and fine-tune large language models (LLMs) using frameworks like Hugging Face and PyTorch.

Implement LoRA and quantization techniques (4-bit, 8-bit) to optimize model performance and deployment.

Build text, image, and code generation pipelines tailored to business needs using open-source and API-based models.

Integrate generative AI capabilities into applications using APIs (OpenAI, Anthropic, Google Gemini, etc.).

Develop retrieval-augmented generation (RAG) systems for domain-specific chatbots and assistants.

Ensure responsible AI deployment with attention to data privacy, bias mitigation, and content safety.