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Vetted Transformers Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in scalable ML systems and LLM applications
Entry-level Machine Learning Engineer specializing in NLP and LLM systems
Senior Software Developer specializing in AI-driven healthcare and farm management platforms
Mid-level AI/ML Engineer specializing in NLP, GenAI, and fraud/risk analytics
Senior Software Developer specializing in cloud-native microservices and GenAI/ML
Mid-level Machine Learning Engineer specializing in Generative AI, LLMs, and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and cloud deployment
Mid-level Machine Learning Engineer specializing in MLOps, fraud detection, and data security
Mid-level Machine Learning & AI Engineer specializing in LLMOps, digital twins, and RL
Junior Software Engineer specializing in full-stack development and applied ML
Mid-level Data Analyst/Data Engineer specializing in machine learning and NLP
Mid-level Data Science & AI/ML Engineer specializing in MLOps, NLP, and computer vision
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Junior Machine Learning Engineer specializing in healthcare AI and GenAI RAG
Mid-level AI Software Engineer specializing in healthcare and agentic systems
Mid-level AI/ML Engineer specializing in FinTech and production ML systems
Drashti Magia
Junior AI Engineer specializing in LLMs, multimodal ML, and applied machine learning
“Software engineer with a disciplined, production-minded approach to AI-driven development: uses ChatGPT, Claude, GitHub Copilot, and scoped coding agents to accelerate delivery without giving up architectural judgment. Notably applied a multi-agent workflow on ClinicOps Copilot, using agents for planning, Bedrock/RAG scaffolding, and failure testing while personally owning architecture, grounding quality, and end-to-end review.”
Shreevershith Kollabettu
Mid-level AI Engineer specializing in LLMs, agentic systems, and MLOps
“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”
Adithya Gururaj
Junior AI Engineer specializing in agentic AI, RAG, and voice/telephony systems
“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”