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Vetted spaCy Professionals
Pre-screened and vetted.
Mid-Level Full-Stack Software Engineer specializing in Healthcare and E-commerce
Mid-level Software Engineer specializing in backend microservices and ML inference
Mid-level Data Scientist & AI Engineer specializing in healthcare and financial risk analytics
Mid-level Data Scientist specializing in GenAI, NLP, and recommendation systems
Mid-level Software & ML Engineer specializing in cloud data platforms and MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable ML platforms
Mid-level AI/ML Engineer specializing in LLMs, NLP, and scalable ML pipelines
Mid-level Data Scientist specializing in ML, NLP, and production AI workflows
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Staff-level AI/ML Engineer specializing in enterprise RAG, agentic automation, and AI governance
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
Mid-level AI/ML Engineer specializing in conversational AI, NLP, and LLM-powered RAG systems
Jayeetra Bhattacharjee
Mid-level AI/ML Engineer specializing in LLMs, NLP, and analytics automation
“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”
Mid-level Software Engineer specializing in SRE, observability, and LLM-powered automation
Naga Gayatri Bandaru
Mid-level AI/ML Engineer specializing in MLOps and production ML systems
“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”