.png){kind=logo}
Vetted Hyperparameter Tuning Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Agentic AI & ML Engineer specializing in LLM agents and RAG systems
Senior Data Scientist specializing in predictive modeling and recommendation systems
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Mid-level Data Scientist specializing in FinTech and product analytics
Mid AI/ML Engineer specializing in LLM alignment and scalable AI systems
Likhitha Bethi
Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI
“Goldman Sachs engineer who owned end-to-end features for an internal onboarding and case management platform, spanning React/TypeScript UI, a GraphQL gateway, and Node + Spring WebFlux microservices. Built and operated a Kafka-based ingestion and search pipeline with DLQs, retries, idempotency, and strong observability, and improved developer experience via backward-compatible GraphQL API design and schema-driven documentation.”
Yue Yang
Intern Data Scientist specializing in GenAI (LLMs, RAG) and ML model optimization
“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.”
Muhan Zhang
Junior AI Software Engineer specializing in LLM pipelines, OCR, and RAG
“Built and shipped a production LLM pipeline for nursing home Medicare reimbursement (PDF OCR + fact extraction + keyword RAG + QA) that reportedly increased payouts by ~$1K/month per patient. Strong in LLM ops/benchmarking (ground truth, LLM-as-judge, cost/I-O tracking) and pragmatic optimization—swapped retrieval approaches, fine-tuned a small model to cut OCR cost 90%, and migrated workloads to Azure/Temporal to scale nightly processing 10x.”
KaMing Cheung
Junior Software Engineer specializing in full-stack and machine learning
“CMU IoT coursework project builder who implemented an end-to-end TinyML gesture recognition system on a Particle Photon + ADXL345, streaming data via MQTT/Node-RED to a real-time Node.js frontend and deploying a quantized logistic regression model on-device. Also explored multi-drone coordination, implementing leader-follower offset control and a pivot/arc turning strategy to avoid collisions, and brings practical Docker/Kubernetes plus CI/CD workflow experience from internships.”
Lakshmi Narayana
Mid-level Data Science AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and recommendation systems
Mid-level Data Engineer specializing in analytics, BI dashboards, and ETL pipelines
Mid-level AI/ML Engineer specializing in fraud detection and customer lifetime value modeling
Entry-Level Data Scientist specializing in Applied Analytics and Machine Learning
Junior Software Engineer specializing in distributed systems and AI evaluation
Junior Software Engineer specializing in ML, computer vision, and data engineering
Mid-level Data Scientist specializing in LLMs, RAG, and personalization
Mid-level Data Scientist specializing in ML, NLP, and fraud/anomaly detection