AI Software Engineer | AI/ML | Generative AI | Python | C#/.NET | SQL | RAG | LLMs

• Built and deployed production AI/ML and Generative AI applications using Python, PyTorch, TensorFlow, Hugging Face, and Azure OpenAI for document understanding and enterprise content intelligence.

• Developed LLM-powered applications and Retrieval-Augmented Generation (RAG) pipelines using embeddings, retrieval workflows, prompt engineering, and REST API integrations.

• Built and maintained machine learning classification services, improving classification accuracy by approximately 20–25% through model optimization, evaluation, and data validation.

• Designed automated model evaluation and regression testing workflows to monitor accuracy, latency, reliability, and production performance.

• Developed production inference services and optimized model performance through batching, quantization, and inference optimization techniques.

• Integrated AI services with enterprise applications and data sources through REST APIs and backend service integrations.

• Deployed and managed machine learning workloads using Azure ML, MLflow, Docker, Kubernetes, and CI/CD pipelines.

• Collaborated with engineering and business teams to translate technical requirements into scalable AI solutions and production-ready services.

• Troubleshot production issues involving APIs, models, data pipelines, dependencies, and deployment infrastructure while maintaining system reliability.

• Developed and deployed production machine learning models and inference pipelines for real-time autonomous driving perception and prediction systems.

• Built computer vision solutions using Python, PyTorch, and OpenCV for object detection, tracking, and sensor-fusion workflows.

• Processed and analyzed large-scale LiDAR, radar, and camera datasets to support model training, evaluation, and validation.

• Optimized distributed GPU training workflows, reducing training time by approximately 30% through performance and infrastructure improvements.

• Developed model evaluation and validation workflows to measure accuracy, performance, and reliability across large datasets.

• Built scalable ML pipelines using Spark and integrated production workflows with Docker, Kubernetes, MLflow, and Google Cloud Vertex AI.

• Collaborated with software and data engineering teams to troubleshoot production ML pipelines and improve model deployment reliability.

• Used Git-based development and CI/CD practices to support reproducible machine learning development and deployment.

• Built and maintained ETL/ELT data pipelines using Python, Apache Spark, and Airflow to process high-volume transactional, inventory, and operational data.

• Designed data integration workflows connecting multiple sources, including Oracle, DB2, MongoDB, PostgreSQL, SQL Server, and Snowflake.

• Developed complex SQL queries, stored procedures, transformations, and data validation processes for analytical and operational workloads.

• Optimized SQL queries and stored procedures, improving database and pipeline performance by approximately 25%.

• Built data models and centralized data workflows to support analytics, reporting, and business intelligence requirements.

• Implemented real-time data processing and streaming pipelines using Apache Kafka.

• Performed data quality checks, validation, transformation, and reconciliation across multiple source systems.

• Worked with Snowflake for cloud-based data warehousing, SQL transformations, analytical workloads, and reporting data preparation.

• Used Git, GitHub Actions, Docker, and Kubernetes to support development, automation, and deployment of data engineering workflows.

• Collaborated with stakeholders and engineering teams to troubleshoot data issues, improve pipeline reliability, and deliver scalable data solutions.