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Vetted Vector Databases Professionals
Pre-screened and vetted.
Junior AI/ML Engineer specializing in LLMs, RAG, and multimodal agents
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
Senior Software Engineer specializing in backend microservices and AI/ML integrations
Junior ML Engineer specializing in GenAI agents, RAG, and computer vision
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Mid-level AI/ML Engineer specializing in Generative AI and RAG systems
Director of Engineering specializing in GenAI and scalable enterprise platforms
Intern AI/Data Science Engineer specializing in LLM agents, data engineering, and predictive analytics
Senior Software Engineer specializing in cloud-native backend, ETL, and AI/ML on AWS
Mid-level AI/ML Engineer specializing in NLP, MLOps, and financial risk & fraud analytics
Mid-level AI Engineer specializing in Generative AI and LLM/RAG systems
Senior Data Scientist specializing in LLMs, NLP, and anomaly detection
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Senior Python Backend Engineer specializing in Django, APIs, and AI automation
Junior Full-Stack/Cloud Engineer specializing in AI and data-driven applications
Mid-level AI/ML Engineer specializing in forecasting, MLOps, and generative AI
Principal Data Scientist specializing in LLMs, RAG, and enterprise AI products
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.”
Swathi Sankaran
Senior Python Full-Stack Developer specializing in cloud, data engineering, and ML/GenAI
“Backend/data engineer with hands-on production experience building FastAPI services on AWS and implementing strong reliability/observability (CloudWatch, ELK, correlation IDs, alarms). Has delivered serverless + container solutions with IaC (CloudFormation/Terraform) and Jenkins CI/CD, and built AWS Glue/PySpark pipelines into S3/Redshift with schema-evolution and data-quality safeguards; demonstrated large-scale SQL tuning (45 min to 3 min on a 500M-row workload).”
silin liu
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and enterprise ML systems
“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”