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Vetted SHAP Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in LLM applications, RAG, and MLOps
Junior AI Engineer specializing in NLP, computer vision, and MLOps
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 AI/ML Engineer specializing in NLP, MLOps, and scalable ML platforms
Junior Data Scientist specializing in applied machine learning and analytics
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Mid-level AI/ML Engineer specializing in NLP, LLMs, and MLOps
Staff-level AI/ML Engineer specializing in enterprise RAG, agentic automation, and AI governance
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Senior Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level AI/ML Developer specializing in FinTech fraud detection and GenAI assistants
Mid-level Data Scientist specializing in financial ML, NLP, and MLOps
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.”
Aakash Mahesha
Junior AI/ML Engineer specializing in anomaly detection and LLM/RAG systems
“Built and productionized a tool-first, multi-agent framework that augments an anomaly detection model with domain context to generate trustworthy, evidence-backed anomaly explanations (including false-positive likelihood). Architected the platform to be model/orchestration/vectorDB agnostic (e.g., GPT + CrewAI + ChromaDB vs Claude + LangGraph + other vector DB) with strong performance, reliability, and OpenTelemetry-based observability. Also built a personal LangGraph-based "mock interviewer" agent that asynchronously fuses voice + live code input using state reducers, stop conditions, and fallback routing.”