● Developed scalable Python-based data pipelines for residential solar energy analytics on GCP.

● Collaborated with ML engineers to evaluate LLM-based assistants for summarizing device health logs.

● Deployed a containerized backend service on Kubernetes, implementing rolling updates and health checks.

● Reduced manual effort by 40% by automating BigQuery data ingestion via Google Cloud Functions.

● Improved pipeline reliability using Airflow (Google Cloud Composer) for workflow orchestration.

● Enhanced pipeline performance using Apache Spark for distributed analytics.

● Strengthened system security by implementing GCP authentication mechanisms and IAM policies.

● Fostered technical alignment by collaborating with product managers and data scientists

● Configured AWS ECS/EC2 clusters for scalable infrastructure reducing deployment time by 50%.

● Transformed release cycles by optimizing CI/CD pipelines with GitHub Actions.

● Deployed and managed containerized microservices using Docker and Kubernetes.

● Automated ETL processes by building Apache Airflow pipelines for data handling.

● Contributed on AI-powered audience extraction using LLM for keyword matching and user segmentation.

● Worked with embedding-based search to boost social media profiles via semantic similarity.

● Optimized inter-service communication by designing RESTful APIs within a service-oriented architecture.

● Grew Twitter Audience Builder user base from 2M to 20M.

● Built robust front end UI using NextJS and TypeScript.

● Decreased request latency by 25% by integrating backend APIs with Node.js scripts

• Assisted in developing and maintaining ETL/ELT pipelines to ingest and transform structured and semi-structured data.

• Wrote Python and SQL scripts to clean, validate, and prepare datasets for analytics and reporting.

• Supported data workflows using Apache Spark and workflow orchestration tools such as Airflow/Composer.

• Helped optimize queries and data transformations to improve pipeline reliability and performance.

• Collaborated with senior data engineers and analysts to understand requirements and implement data solutions.

• Performed data quality checks, monitoring, and debugging to ensure accuracy and consistency of data.

• Devised a Python-based NLP assistant for IBM clients to automate customer support with tailored responses.

• Engineered custom Chatbots utilizing IBM Watson to enhance internal workflows for calendar scheduling.

• Implemented Decision Trees/Random Forests models to increase response efficiency of NLP assistant by 30%.

• Developed an advanced React-based front-end for an AI chatbot, with a user-friendly interface.

• Implemented NodeJS scripts to allow seamless integration of the NLP assistant with backend APIs.

• Enhanced user support systems and conducted post-incident analysis to refine service responses.