Machine Learning Engineer and Software Developer

Kashish Shah

San Francisco, CA +1-857-***-**** *************@*****.*** LinkedIn GitHub Portfolio EDUCATION

Northeastern University Boston, MA

Master's, Electrical and Computer Engineering, GPA: 3.64 WORK EXPERIENCE

SquareResults Phoenix, AZ

Machine Learning Engineer Aug 2025 - Present

● Enhanced matching pipeline efficiency 41% (1.2M 1.7M records/hour) using SQL diagnostics and runtime profiling, supporting 500K+ daily recommendations with 99.5% uptime.

● Constructed automated checkpoints with Airflow, reducing reprocessing and stabilizing data flows for 2M+ peak API requests. Adaptive Concepts Academy Troy, MI

Software Development Engineer Aug 2025 - Present

● Architected a multilingual log pipeline with asyncio and PySpark, processing 10M+ daily events across distributed systems with 2.3 throughput gain and 38% lower p95 latency.

● Developed benchmarking suite using PyTorch Profiler and Intel VTune across 50+ model architectures in evaluation-heavy pipelines, increasing inference consistency 31% under production loads.

● Deployed Prometheus/Grafana telemetry detecting degradation 3 days earlier, improving MTTR 45%. KAP Ventures Los Angeles, CA

Machine Learning Engineer May 2025 - Aug 2025

● Optimized Spark pipeline processing 50M+ daily events for model training and analytics, achieving 39% performance gain by repartitioning skewed joins and reducing training time from 4hrs to 2.5hrs.

● Established Terraform/Databricks IaC automation for reproducible deployments reducing provisioning from 3 days to 4 hours while cutting configuration drift 45% across 8 clusters.

● Improved personalization CTR 12% with SQL monitoring dashboards (Looker+Redshift) surfacing quality issues 72hrs earlier. Institute of Experiential AI, Northeastern University Portland, ME Assistant AI Researcher Aug 2024 - Dec 2024

● Automated preprocessing pipeline for 3D motion data using NumPy/Pandas/OpenCV, processing 18GB sensor recordings with 33% improved accuracy and 52% less manual work.

● Implemented datasets with MLflow and DVC handling 200GB+ across 15 experiments, accelerating iteration cycles 70%. Northeastern University Boston, MA

Data Scientist - Assistant Researcher Mar 2024 - Jun 2024

● Engineered ML infrastructure with TensorFlow/MLflow/DVC for 500+ clinical samples, scaling to 200GB+ via PySpark/Airflow and reducing pipeline failures 43%.

● Streamlined preprocessing with vectorized Pandas and Dask, reducing feature engineering from 6 hours to 45 minutes per run. Kleren Oak Group Remote

Full-Stack Software Engineer Apr 2021 - Mar 2023

● Delivered React component library and MongoDB schemas for multiclient platforms deployed across 5 client platforms, increasing engagement 29% and reducing UI development time 40%.

● Strengthened backend resilience 26% by optimizing MySQL queries with proper indexing, implementing 85%+ Jest coverage across critical paths, and establishing Sentry error tracking for debugging cycles shortened 40%. PROJECTS

MedConnect: LLM Powered Clinical Data Retrieval and Summarization Platform

● Designed a FHIR-compliant retrieval system using LangChain/Neo4j/FastAPI, achieving 89% query relevance and reducing clinician lookup time from 8 to 4.8 minutes across 1,000+ queries.

● Orchestrated versioned ETL with MLflow/DVC/Airflow processing 200GB+ patient records (500K+ patients) with automated validation and rollback capabilities.

TECHNICAL SKILLS

• Programming Python, TypeScript, JavaScript, SQL, Bash

• Machine Learning and AI PyTorch, TensorFlow, Scikit learn, XGBoost, Transformers, LangChain

• Data Science and Analytics Pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, Statistical Analysis, Hypothesis Testing

• Data Engineering Airflow, Spark, Kafka, Flink, ETL and ELT Pipelines, Data Warehousing

• MLOps and Experimentation MLflow, DVC, Model Deployment, Feature Stores, Experiment Tracking

• Backend and APIs FastAPI, Flask, Node.js, REST, GraphQL, Microservices, Message Queues

• Cloud and Infrastructure AWS EC2, Lambda, S3, ECS, EKS, Docker, Kubernetes, Terraform

• Databases PostgreSQL, MySQL, MongoDB, DynamoDB, Redis, Neo4j, ClickHouse

• Testing and Monitoring Pytest, Unit Testing, Integration Testing, Prometheus, Grafana, team player, Oracle.

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