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Vetted Data Engineering Professionals
Pre-screened and vetted.
Mid-level Full-Stack Developer specializing in Python, React, and cloud-native AI microservices
Junior AI/ML Engineer specializing in LLMs, RAG, and multimodal agents
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level Data Scientist specializing in ML, MLOps, and forecasting for FinTech and AI hardware
Mid-level AI/ML Engineer specializing in NLP, MLOps, and compliance-focused ML systems
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
Intern AI/Data Science Engineer specializing in LLM agents, data engineering, and predictive analytics
Mid-level Software Engineer specializing in distributed systems and applied ML
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and RAG for financial services
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Senior Data Engineer specializing in Cloud Data Platforms and Generative AI
Junior Data Analyst specializing in finance, supply chain, and GTM analytics
Intern Machine Learning Engineer specializing in LLMs, retrieval, and vision-language models
Principal Data Scientist specializing in AI/ML forecasting and MLOps
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
Mid-level Data Engineer specializing in GCP, Spark, and healthcare analytics
Mid-level Backend/Data Engineer specializing in legal data pipelines and APIs
Adithya Rajendra
Junior Data Engineer specializing in Azure data platforms and GenAI analytics
“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”
Caden Cheah
Intern Full-Stack/ML Engineer specializing in LLM applications and mobile development
“Backend engineer who built a serverless AWS Lambda microservices backend for a parenting assistance mobile app, including a personalized recommendation system optimized to sub-500ms via precomputed scoring and DynamoDB caching. Demonstrates strong production pragmatism: CloudWatch-driven performance tuning (provisioned concurrency), zero-downtime phased schema migrations, and robustness patterns like optimistic locking and request deduplication. Also led a refactor of an LLM RAG pipeline to improve retrieval quality and cut latency from ~5s to ~3s.”
Timothy Wong
Mid-level Data Engineer specializing in experimentation, analytics, and AI-driven product experiences
“Built production LLM automations using the Claude API, including a sales enablement workflow that summarizes playbooks and incorporates sales call metadata into strategic one-pagers. Experienced in orchestrating and scheduling data pipelines with SnapLogic, Airflow, and Databricks, and in scaling LLM API calls via parallel/batch processing. Also partnered with HR to deliver prompt-tuned, automated Slack messaging aligned to business tone and acceptance criteria.”
“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”