Open to opportunities

Ashar Baig

@aimlengineer

Message

Senior AI/ML engineer translating research into production-grade systems.

United States

Message

What I'm looking for

I seek a role owning end-to-end ML systems where I can apply deep learning, MLOps, and cloud deployment to deliver scalable, reliable AI solutions and continue technical growth in production ML and research translation.

I am a Senior AI/ML Engineer with 8+ years building production-grade machine learning systems, specializing in deep learning, NLP, reinforcement learning, and MLOps. I consistently take end-to-end ownership from research and prototyping through deployment, monitoring, and iterative optimization to deliver measurable business and operational outcomes.

I have designed scalable ML pipelines and infrastructure using Python, PyTorch, TensorFlow, Docker, Kubernetes, MLflow, and cloud platforms (AWS, Azure, GCP), and have applied data fusion, model evaluation, explainability, and bias analysis to real-world multimodal applications. I collaborate cross-functionally to prioritize mission-aligned capabilities and ensure reliable, maintainable AI solutions in edge, hybrid, and cloud environments.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Gecko Robotics

Feb 2022 - Present (3 years 10 months)

Led end-to-end development and production deployment of ML features, building scalable workflows and containerized inference pipelines that improved real-time decision-making and operational model performance.

Python PyTorch TensorFlow Docker MLOps NumPy Pandas Model Monitoring

Data Scientist

Kensho Technologies

Aug 2019 - Jan 2022 (2 years 5 months)

Conducted applied R&D on multimodal entity/event/topic extraction and built transformer-based models and reproducible pipelines to transition research models into production-ready analytics.

Deep Learning Transformers Python Docker Model Evaluation Reinforcement Learning

Machine Learning Engineer

Descartes Labs

Jun 2017 - Jul 2019 (2 years 1 month)

Designed and productionized large-scale ML pipelines and MLOps infrastructure (MLflow, Airflow, Kubernetes) to enable reliable training, deployment, and automated monitoring across cloud environments.