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Vetted SHAP Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM agents, search/recommendation, and MLOps
Mid-level Software Engineer specializing in AI/GenAI and cloud-native backend systems
Mid-level AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level AI/ML Engineer specializing in MLOps, fraud detection, and predictive analytics
Mid-level Data Scientist specializing in Healthcare ML and Generative AI
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI
Junior Generative AI Engineer specializing in LLM fine-tuning and RAG pipelines
Mid-level AI Engineer specializing in NLP, LLM fine-tuning, and RAG systems
Junior Data Scientist / AI Engineer specializing in analytics, ML, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI Engineer specializing in LLM automation and RAG systems
Mid-level AI/ML Engineer specializing in MLOps, streaming data, and NLP/CV
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM systems
Vikesh Patel
Senior AI/ML Engineer & Data Scientist specializing in LLMs, RAG, and MLOps
“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”
srisindhu manchikanti
Mid-level Machine Learning Engineer specializing in multimodal and time-series AI systems
“Backend engineer who rebuilt and refactored high-traffic systems at Phenom using Java/Spring Boot/Play and also designs Python/FastAPI services. Focused on measurable reliability and performance gains through DB/query optimization, async processing, and strong observability, with disciplined rollout practices (feature flags, parallel runs, rollback) and security patterns including token auth and row-level security.”
Santhi Sampath Gamidi
Mid-level AI Engineer and Data Scientist specializing in LLM agents and RAG systems
“Built a production-grade LLM evaluation and regression system that stress-tests models across hundreds of iterations, combining LLM-as-judge, semantic similarity, statistical metrics, and rule-based checks, with results delivered via stakeholder-friendly HTML reports and dashboards. Experienced orchestrating multi-agent RAG workflows using LangChain/LangGraph and event-driven GenAI pipelines in n8n integrating OCR, speech-to-text, and external APIs, with strong emphasis on reliability, observability, and explainable failures.”
Sree Manasa Vuppu
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG
“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”
Vedasahaja bandi
Mid-level AI Engineer specializing in NLP, computer vision, and healthcare analytics
“Data scientist who has built production LLM agents (GPT-4o + LangChain + RAG) to automate analyst-style ad hoc CSV analysis with guardrails and GPT-as-a-judge evaluation. Also delivered an explainable healthcare NLP system for ICD code classification by collaborating closely with clinicians, using a hybrid rule-based decision tree + BERT model to reach 97% accuracy and cut manual review time.”
Karthik Patralapati
Mid-level AI/ML Software Engineer specializing in GPU-optimized LLM inference and cloud microservices
“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”