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Vetted NLTK Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in GenAI, NLP, and recommendation systems
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable ML platforms
Junior Robotics & AI Engineer specializing in SLAM, motion planning, and sim2real learning
Mid-level AI/ML Engineer specializing in LLMs, NLP, and scalable ML pipelines
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
Junior Research Data Scientist specializing in healthcare analytics and real-world evidence
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
Senior Machine Learning Engineer specializing in MLOps and Generative AI
Mid-Level Software Engineer specializing in Java/Spring Boot microservices and cloud DevOps
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.”
Julian Lee
Intern Software Engineer specializing in AI/LLMs and full-stack development
“AI/ML infrastructure-focused engineer who has built production RAG systems from scratch (Supabase/pgvector + OpenAI embeddings) and iterated using formal eval metrics to improve retrieval quality. Also debugged real-time audio issues in a LiveKit-based pipeline by correlating packet loss with VAD behavior, and has deep experience building brittle, customer-specific financial platform integrations in Python/Playwright (2FA, redirects, token refresh, rate limits).”
Vamsi Koppala
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”
Nikitha Kommidi
Mid-level AI/ML Engineer specializing in fraud detection, NLP, and MLOps
“Built a production real-time fraud detection and customer-support automation platform at Citibank, tackling extreme class imbalance (reported ~1:5000) and strict latency constraints. Combines hands-on MLOps (Airflow, Kubernetes, MLflow; Snowflake/Spark/S3 integrations; CI/CD model promotion) with cross-functional delivery to Risk & Compliance focused on interpretability and reducing false positives.”
shiva kumar kotha
Mid-level AI/ML Engineer specializing in Generative AI and healthcare data
“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”
Cristian Vega
Senior AI/ML Engineer specializing in Generative AI and RAG
“ML/NLP practitioner at Morf Health focused on unifying fragmented healthcare data by linking structured patient/encounter records with unstructured clinical notes. Has hands-on experience with transformer embeddings, vector databases, and domain fine-tuning, plus rigorous evaluation (precision/recall) and human-in-the-loop validation with clinical SMEs to make pipelines production-grade.”
Sai Ganesh nelluri
Mid-level Generative AI Engineer specializing in LLM systems and RAG
“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”