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Vetted AI Engineers
Pre-screened and vetted.
Sreenuvasulu B
Mid-level AI/ML Engineer specializing in recommendations, NLP, computer vision, and MLOps
Avish Modi
Mid-level AI Engineer specializing in Generative AI, RAG, and MLOps
Yeshwanth Pulapa
Mid-level AI/ML Engineer specializing in Databricks, MLOps, and real-time fraud detection
Jayesh Santosh Zambre
Mid-level AI Engineer specializing in LLM agents, RAG, and knowledge graphs
SaiKousthubhaDas Kalvakolanu
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Amir Pasha
Senior Machine Learning Engineer specializing in LLMs, agentic AI, and MLOps
Ankur Chemburkar
Mid-level AI Engineer specializing in LLM agents and multimodal generative AI
Shouvik Ghosh
Senior AI/ML Engineer specializing in LLM applications, RAG systems, and MLOps
Venkat Pruthvi Ganji
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Prudhvi Angirekula
Mid-level AI Engineer specializing in LLM agents, RAG, and enterprise GenAI
Soumya Gade
Mid-level AI Engineer specializing in LLM agents and RAG systems
Haripavan Madamanchi
Mid-level AI/ML Engineer specializing in LLM and production ML systems
Sid Ghodke
Executive Engineering Leader specializing in Telehealth Platforms and Healthcare IT
Ayush Gupta
Mid-level AI Engineer specializing in Agentic AI and Generative AI
“Built and deployed a live LLM-powered platform that takes a LinkedIn job URL + resume and generates job-specific resumes and personalized outreach at scale, with production-grade logging/monitoring/retries on Vercel + Railway. Experienced with agent orchestration (AWS Bedrock/Strands, LangGraph, CrewAI) and rigorous AI workflow testing, plus stakeholder-facing prototypes like data lineage/metadata and NL-to-SQL + dashboard generation.”
Sayuj Shah
Mid-level Data Analyst & AI Practitioner specializing in ML, LLMs, and analytics platforms
“Data Analyst at U.S. Cellular who built production LLM solutions, including a Tableau-embedded chatbot that converts natural language questions into Oracle SQL and returns actionable KPI insights for non-technical users. Also authored MAD-CTI, a multi-agent LLM system for dark web hacker forum threat intelligence (published in IEEE Access) that outperformed single-agent approaches by 14%.”
Pranav Thorat
Mid-level Machine Learning Engineer specializing in MLOps and applied AI
weixin wang
Junior AI Engineer specializing in computer vision, LLM infrastructure, and generative media pipelines
Yaswanth Reddy alla
Mid-level AI Engineer specializing in Generative AI and LLM/RAG systems
silin liu
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and enterprise ML systems
“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”
Anvith Reddy Dodda
Mid-level AI Engineer specializing in GenAI, NLP, and MLOps
“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”
Aashna Kunkolienker
Junior AI Engineer specializing in agentic workflows and ML platforms
“Building a production LLM/agent system for a leading US dental provider that extracts rules from payer handbooks/portals and EDI 271 responses to validate and improve patient cost estimates. Combines GCP stack (BigQuery, GKE, Cloud Run, Pub/Sub, Vertex AI) with strong agent reliability practices (observability, validator agents, grounding, PII/hallucination guardrails, confidence scoring) and has led non-technical customer stakeholders on enterprise ServiceNow↔Aha sync and AI-powered enterprise search/summarization.”
Christopher Song
Junior AI/ML Engineer specializing in real-time computer vision and tracking systems
“Full-stack engineer who built and owned a production real-time computer-vision inference platform at Credence, spanning Next.js App Router/TypeScript frontend with SSE/WebSocket streaming, a Flask backend, and Postgres analytics. Demonstrated measurable performance wins (70% fewer re-renders; latency cut to ~40–50ms) and strong production rigor (durable orchestration, idempotency, observability, AWS EC2 + CI/CD) with tight post-launch UX iteration based on analyst feedback.”
Siva Sai Kumar Mogalluru
Mid-level AI Engineer specializing in Generative AI, MLOps, and NLP for finance and healthcare
“Built and deployed a secure, production LLM-based document summarization and risk-highlighting tool for financial auditors, running inside a private Azure environment to protect confidential data. Focused on reliability (hallucination mitigation via retrieval-based prompts and source citations) and validated performance through comparisons to auditor summaries plus a user pilot, cutting review time by about half.”