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Vetted Streamlit Professionals
Pre-screened and vetted.
Senior AI/ML Software Engineer specializing in LLMs, NLP, and scalable ML platforms
Senior Applied ML Scientist specializing in LLMs, ads ranking, and RAG systems
Senior AI/ML Engineer & Data Scientist specializing in NLP, entity resolution, and knowledge graphs
Executive Technology Leader specializing in Cloud, Data Platforms, and AI/ML
Intern Software Engineer specializing in ML and data pipelines
Mid-level Machine Learning Engineer specializing in LLM personalization and scalable MLOps
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
Intern Software Engineer specializing in ML and data pipelines
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems
Daksh Adhar
Junior Robotics & Reinforcement Learning Engineer specializing in dexterous manipulation
“Robotics software engineer (master’s student) who placed 3rd in the CMU VLA challenge and presented at IROS, building an LLM-powered language system (Gemini 2.5) for mobile-robot scene Q&A and language-based navigation. Hands-on ROS1/ROS2 experience including ros2_control + PILZ planning for a KUKA arm, plus simulation (Gazebo) and containerized submissions with Docker.”
Nikhil Reddy
Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms
“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”
Krishna Sahith Poruri
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
Senior Full-Stack Python Developer specializing in cloud-native RAG and microservices
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native AI systems
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems