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Vetted SHAP Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in Generative AI and NLP
Junior Software Engineer specializing in AI/ML and full-stack development
Mid-level AI/ML Engineer specializing in predictive maintenance, computer vision, and LLM/RAG systems
Mid-level AI/ML Engineer specializing in NLP and MLOps for regulated industries
Senior Data Scientist specializing in Generative AI, NLP, and ML for banking and healthcare
Mid-level AI Engineer & Data Scientist specializing in Generative AI, NLP, and Cloud ML
Mid-level Data Scientist specializing in LLMs and NLP for financial analytics
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Senior Data Engineer specializing in real-time pipelines, cloud data platforms, and healthcare analytics
Senior Data Scientist specializing in AWS ML solutions for healthcare, telecom, and e-commerce
Senior AI/ML Engineer specializing in LLMs, NLP, and production MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior Machine Learning Engineer specializing in GenAI, LLMs, and MLOps
Senior AI Architect specializing in Generative AI and LLM systems
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
Pravallika Kilari
Mid-level AI/ML Engineer specializing in NLP, GenAI, and MLOps in healthcare and finance
“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”
Anvesh Reddy Narra
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”
Chandan Chalumuri
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”