Vetted Machine Learning Professionals

Andrew Bender

Screened

Executive product leader specializing in EdTech and SaaS

Cambridge, MA20y exp

“Product leader with deep edtech and AI experience who helped scale instaGrok from a disappointing launch to 330,000 monthly users in one school year without paid acquisition. Has operated as the sole product decision-maker in a tiny startup, consulted through founder-strategy conflict, and brings a human-centered view of AI as a tool for exploration and learning rather than replacement.”

Bharti Prasad

Screened

Executive growth and marketing leader specializing in consumer internet and gaming

Austin, TX15y exp

“Growth and GTM leader with end-to-end P&L ownership across e-commerce and real-money gaming, including scaling a fantasy sports product to the #4 player in its category and managing a 10M-user business. Particularly notable for launching a Tongits app in the Philippines remotely from India using self-directed market research, then doubling retention in 3 months and scaling it to #5 in the country.”

“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”

“Safety-critical firmware validation engineer with Tesla autonomous vehicle experience who built Python-based HIL/SIL automation and dashboards, cutting regression time by 30% while maintaining an auditable risk-tradeoff process with safety and engineering teams. Also deployed an inventory management system across 8+ R&D teams in 3 countries at FUJIFILM, troubleshooting a major cross-site sync issue to a timezone root cause with strong documentation and interim mitigations.”

“Full-stack engineer with deep serverless AWS experience who built VidToNote, an AI video analysis platform, end-to-end using Next.js App Router/TypeScript and an event-driven pipeline (API Gateway, Lambda, DynamoDB, S3, Step Functions, SQS). Strong on production reliability and observability (CloudWatch, X-Ray, structured logging), plus data/analytics work in Postgres with measurable query optimizations and durable LLM evaluation workflows. Amazon background; integrated 22 AWS services and completed AWS Solutions Architect Professional certification within a month.”

“Full-stack engineer in the investment tracking/financial reporting space who built an automated reporting dashboard and compliance/reporting pipeline end-to-end using Next.js (App Router, server/client components), REST, and Postgres. Demonstrated measurable performance wins (~30% faster loads) through caching and query optimization, and built durable orchestrated workflows in n8n with retries, idempotency, and reconciliation checks.”

Andrew Liang

Screened

Intern Software Engineer specializing in full-stack and AI/ML systems

2y exp

“Software engineer with experience at Amazon and Agora building end-to-end systems: a knowledge-base AI chatbot (React/TypeScript UI + retrieval/response backend + Docker deployment) and an internal approval governance platform using AWS Step Functions and DynamoDB. Emphasizes fast iteration without sacrificing trust via feature-flag rollouts, citation-required answers, abstention on low-confidence retrieval, regression query sets, and strong observability (request IDs, structured logs, latency/error monitoring).”

“Early-career engineer (internship background) who built a production-style notes product using Next.js App Router with Server Components/Server Actions and a Postgres-backed analytics model. Demonstrates strong performance and reliability instincts—measured DB latency improvements via indexing and cursor pagination, plus durable orchestration with Temporal using idempotency and deterministic workflows.”

Jacqueline Zhang

Screened

Mid-level Machine Learning Engineer specializing in LLMs, fairness, and healthcare ML

Illinois, USA4y exp

“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”

Param Yanamandra

Screened

Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation

Campbell, CA21y exp

“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”

Yeshwanth Sai Pala

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech

Remote, USA4y exp

“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”

Vela Sivasankaran

Screened

Executive technology leader specializing in cloud, telecom, and digital transformation

Virginia, USA16y exp

“Former founder of a financial services technology startup that is currently on hold for family reasons. Has hands-on startup fundraising exposure from employee roles, including presenting proof-of-concept demos to venture capital firms, and brings a strong focus on fraud prevention, safeguards, and regulatory compliance.”

“ML/AI engineer with experience at Uber and Scale AI, focused on customer service automation across both classical NLP and generative AI systems. Has owned systems from experimentation through production on AWS, including LLM fine-tuning, RAG optimization, safety evaluation, and internal Python platform tooling that improved consistency and engineering velocity.”

“Technical Account Manager at LinkedIn for 7 years supporting some of the company's largest global enterprise customers across LinkedIn Learning and Recruiter. Blends sales engineering, implementation, compliance, and post-sales ownership, with strong experience navigating complex integrations, migrations, and regulated-industry security reviews. Particularly compelling for enterprise SaaS roles needing someone who can span pre-sales architecture, customer trust, and renewal-critical problem solving.”

“Backend/data engineer with hands-on production AWS experience spanning serverless APIs (Chalice/Lambda/API Gateway/Cognito) and data pipelines (Glue PySpark + Step Functions). Has modernized a legacy SAS reporting system into AWS microservices and implemented schema-drift detection and incident prevention for ETL workflows, plus measurable SQL tuning wins (30 min to <10 min runtime).”

“Internship experience at Intuit building an LLM-grounded QA system for internal microservice data across 100+ microservices, using a graph database approach (evaluated Neo4j and selected AWS Neptune for production alignment). Also has UC Berkeley research experience (including work with Prof. Dawn Song / Berkeley Eye Research Lab) and cross-functional collaboration with bioinformatics/biology teams to deploy software systems on research servers.”

“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”

“Built and deployed a production LLM-powered risk assistant for KPMG and Freddie Mac that lets analysts query a confidential Neo4j risk graph in natural language (no Cypher), turning multi-day analysis into minutes with traceable, cited answers. Implemented rigorous guardrails, deterministic verification, RBAC/security controls, and a full eval/observability stack, cutting query error rate by ~50% and iterating through weekly UAT with non-technical risk analysts.”

Derek Tuggle

Screened

Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls

San Francisco, CA6y exp

“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”

Dheeraj Kumar

Screened

Intern Data Scientist specializing in marketing analytics and data engineering

Tucson, Arizona2y exp

“AI/LLM practitioner with internships at Dell Technologies and Roche who built and deployed a healthcare-focused "Doctor LLM" by fine-tuning Meta Llama 3.2 on healthcaremagic.json, emphasizing safety guardrails to prevent harmful medical advice. Experienced in productionizing AI workflows with monitoring, testing, and orchestration (Airflow, Kubernetes), and in delivering AI-agent-driven competitive landscape insights to non-technical business stakeholders.”

Jingyao Chen

Screened

Junior Backend/Platform Engineer specializing in AI microservices and cloud-native systems

Pittsburgh, PA2y exp

“Cofounder at MeowyAI who shipped a production multimodal (vision/voice/text) AI task manager using Gemini, tackling real-world issues like hallucinations, tool-calling safety, and RAG-based preference memory. Also built a production multi-agent RAG system orchestrated with LangGraph (and contributes to LangChain), with strong emphasis on latency optimization, observability (OpenTelemetry), and rigorous testing/evaluation including A/B tests and adversarial prompting.”

Gurnoor Kaur

Screened

Intern Robotics Software Engineer specializing in motion planning and robot perception

1y exp

“Robotics software engineer with Amazon Robotics internship experience who built a visual-servoing architecture from scratch, navigating multiple simulator pivots to achieve a closed-loop motion-planning and execution prototype. Currently working with ROS 2 on a medical assistive feeding robot using the Kinova Kortex platform (MoveIt2, ros2_control, Gazebo/RViz), and has demonstrated strong real-time debugging and distributed-system synchronization using Carbon and Docker.”

Jehanzeb Khan

Screened

Director-level Engineering Manager specializing in large-scale data and compute platforms

Sunnyvale, CA20y exp

“Platform and distributed-systems leader (player-coach) who owned architecture and reliability for an Amazon analytics/data platform serving ~100K internal users at exabyte scale. Built an ML-driven “Lakeflow” optimization layer that cut pipeline completion times ~20–25% and reduced compute waste >15%, and led major incident response/redesign efforts (e.g., deletion storm) with strong rollout/observability/rollback practices.”