01 / FRAME
Define the signal
Translate ambiguous goals into measurable prediction, ranking, generation or optimization problems.
IT & Technology · AI/ML Engineer
Turn raw data into intelligent systems.
An AI/ML engineer who builds production-grade machine learning, from problem framing and data pipelines to model training, evaluation, deployment and monitoring.
MODEL STATE · CONVERGED
01 · Engineering philosophy
Clarity before complexity.
The best model is not always the most elaborate one. I connect measurable business outcomes with reliable data, reproducible experiments and deployment realities, so intelligence survives contact with production.
02 / LEARN
Build the evidence
Design clean datasets, meaningful features, robust evaluation and experiments that reveal what actually works.
03 / SHIP
Make it reliable
Package models for inference, monitor drift and quality, and create feedback loops for continuous improvement.
02 · Selected systems
Models with a job to do.
Case studies built around real AI/ML engineering concerns: accuracy, latency, explainability, scale and operational health.
01 / Demand intelligence
ForecastMesh
A multi-horizon forecasting pipeline combining temporal features, probabilistic outputs and automated retraining for planning teams.
18% lower forecast error
02 / NLP
SupportLens
Intent classification and retrieval-assisted response routing for high-volume support workflows.
03 / Vision
EdgeSight
Low-latency visual inspection model optimized for constrained edge hardware and dependable inference.
03 · Expertise map
The full ML loop.
A practical toolkit spanning experimentation and production, presented as capabilities rather than generic percentage bars.
04 · Experience path
From experiments to dependable inference.
Production intelligence
Own end-to-end ML systems, from dataset contracts and experimentation to deployment, monitoring and iteration.
Model development at scale
Built predictive and language models, improved evaluation practices and partnered with product and platform teams.
Engineering fundamentals
Developed strong instincts around APIs, testing, observability, data structures and maintainable production software.
05 · Connect
Have a hard signal?
Let's talk about a model that needs to become a product, a data problem that needs sharper framing, or an ML system that needs a more reliable path to production.
✉ arun@example.com
☎ +1 206 555 0171
◈ Seattle, US