Senior AI Engineer

Built an AI-driven computer vision SaaS for industrial quality control, automating real-time defect detection and predictive maintenance across manufacturing environments.

Built a Generative AI platform using LLMs and Transformer-based reasoning, enabling real-time interpretation of industrial telemetry and reducing false alerts by 32%.

Designed an Agentic AI maintenance assistant using LangChain, RAG, and custom embeddings, enabling conversational troubleshooting for technicians.

Implemented streaming and ETL workflows (Kafka, Spark, Airflow) that enriched text-based logs and simulation narratives for downstream modeling.

Created multi-agent inference pipelines with LLM orchestration and optimized models using ONNX Runtime, improving latency by 45%.

Transitioned batch NLP pipelines into continuous-learning systems, enabling real-time adaptation and reinforcement learning loops.

Built deep learning pipelines supporting multi-class segmentation, anomaly detection, and surface-quality scoring using PyTorch and TensorFlow.

Implemented AI monitoring with drift detection, continuous evaluation, and automated retraining cycles to maintain model accuracy.

Mentored engineers on prompt engineering, LLM lifecycle, and agent-based system design, improving team AI maturity.

Key technologies: Python, TensorFlow, PyTorch, FastAPI, Databricks, MLflow, SHAP, LIME, Redis, Azure

Delivered ML-powered fraud detection and campaign optimization systems for global adtech networks handling millions of daily user events.

Built ML pipelines using scikit-learn and TensorFlow to classify fraudulent traffic, bot behavior, and invalid conversions.

Engineered distributed processing pipelines using Kafka and Spark, handling tens of millions of events per hour.

Implemented data validation, drift detection, and A/B evaluation to ensure alignment between offline training and online performance.

Built monitoring systems tracking latency, accuracy decay, prediction throughput, and automated retraining triggers.

Key technologies: Python, scikit-learn, TensorFlow, Flask, Django, Kafka, Spark, AWS SageMaker, GCP AI Platform

Contributed to a scalable sales intelligence and workflow automation platform, improving backend performance, data processing reliability, and enterprise CRM integrations.

Developed backend services and Django REST APIs for lead processing, workflow automation, and CRM synchronization.

Implemented asynchronous pipelines using Celery and Redis, increasing throughput and reducing response latency.

Designed and optimized PostgreSQL schemas with indexing, partitioning, and query tuning to support large analytical datasets.

Integrated third-party CRM systems using REST endpoints, data-mapping logic, and secure authentication mechanisms.

Refactored Python modules to improve maintainability, backend performance, and service reliability.

Supported deployment workflows using Docker, Git, and environment-specific configuration practices.

Key technologies: Python, Django, Django REST Framework, Celery, Redis, PostgreSQL, Docker, Git, REST APIs