.png){kind=logo}
Vetted Model Deployment Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in NLP, predictive analytics, and healthcare/enterprise SaaS
Mid-level Data Scientist / ML Engineer specializing in risk, fraud, NLP and recommender systems
Mid-level Machine Learning/AI Engineer specializing in GenAI, RAG, and LLM inference
Mid-level Software Engineer specializing in microservices, cloud, and FinTech systems
Mid-Level Software Engineer specializing in AI/ML and cloud deployment
Senior Machine Learning Engineer specializing in GenAI and LLM-powered systems
Mid-level AI/ML Engineer specializing in NLP, LLMs, and fraud/AML analytics
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in MLOps and LLM/RAG systems
Mid-level Data Scientist specializing in recommendations, search relevance, and NLP
Senior AI/ML Engineer specializing in MLOps and Generative AI (LLMs/RAG)
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Tushar Jayendra Mhatre
Intern Data Scientist/ML Engineer specializing in generative AI and ML platforms
“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”
Sudheer koki
Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems
“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”
Laxminarayana Yaga
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”
Nagasaikumar Jampani
Mid-level AI/ML Engineer specializing in Generative AI and RAG pipelines
“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”
Mid-level Machine Learning Engineer specializing in MLOps and production ML systems