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Vetted MLflow Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in Generative AI, LLMs, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Senior Unity Engineer specializing in VR and multiplayer mobile games
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
Senior Full-Stack Software Engineer specializing in cloud microservices and data platforms
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level AI/ML Engineer specializing in LLM fine-tuning and RAG for healthcare
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
Junior Machine Learning Engineer specializing in healthcare AI and GenAI RAG
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
Mid-level AI Software Engineer specializing in LLMs, NLP, and MLOps for healthcare
Mid-level AI/ML Engineer specializing in FinTech and production ML systems
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.”
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.”