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Vetted seaborn Professionals
Pre-screened and vetted.
Mid-level AI/ML Data Engineer specializing in MLOps and Generative AI
Mid-level Machine Learning Engineer specializing in multimodal GenAI and RAG systems
Mid-level Software Engineer specializing in Unity game development and XR gameplay
Mid-level Full-Stack Developer specializing in Java/Spring microservices and React/Angular
Mid-level AI/ML Engineer specializing in computer vision and generative AI
Mid-level AI/ML Engineer specializing in generative AI, NLP, and MLOps
Mid-level Data Scientist specializing in deep learning, NLP, and time-series forecasting
Mid-level Data Scientist specializing in NLP, Generative AI, and ML pipelines
Intern Full-Stack Engineer specializing in Java/Spring Boot and React
Junior AI Engineer specializing in NLP, LLMs, and recommender systems
Mid-level Machine Learning Engineer specializing in Generative AI and LLMOps
Junior Software Engineer specializing in cloud-native backend and LLM applications
Mid-level Python Developer specializing in web APIs and cloud deployments
Mid-level Generative AI Engineer specializing in RAG systems and AI-powered education tools
Senior Full-Stack & AI Engineer specializing in FinTech and Healthcare
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Mid-level AI Engineer specializing in agentic LLM workflows and RAG systems
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
Tirth Shah
Mid-level AI/ML Engineer specializing in anomaly detection, data tooling, and cloud-native systems
“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”