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Vetted Streamlit Professionals
Pre-screened and vetted.
Principal Data Scientist & AI/ML Engineer specializing in LLMs, recommender systems, and MLOps
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Sudheer Bommichetty
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”
Pradeep Nadella
Mid-level AI/ML Engineer specializing in LLM optimization and real-time fraud/risk modeling
“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Senior AI/ML Engineer specializing in Generative AI, RAG, and MLOps for FinTech
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”
Deepak Guntreddi
Mid-level Full-Stack Developer specializing in Java/Spring Boot and React
“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”
Senior AI Full-Stack Engineer specializing in GenAI, RAG, and scalable ML systems
Senior Data Scientist / ML Engineer specializing in LLMs, generative AI, and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Abhijay Sai Paladugu
Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning
“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”
Abhinav Bandaru
Junior Data Scientist specializing in Generative AI and agentic LLM systems
“LLM/agentic-systems builder who has shipped production tools for investment research and procurement insights, including a company screener that processes thousands of conference-listed companies using FireCrawl + Google Search + Gemini. Demonstrates strong orchestration expertise (LangGraph multi-agent graphs), performance optimization (async/batching to sub-30s), and pragmatic reliability/evaluation practices with stakeholder-friendly UX (real-time cost tracking and model/parameter toggles).”
Akshitha Singireddy
Junior Software Engineer specializing in data engineering and computer vision
“Former Amazon intern who owned an end-to-end computer vision system to detect package anomalies in fulfillment centers, from data collection/labeling to production deployment on AWS (EC2/S3) with a Streamlit live-monitoring dashboard. Also has ML-in-production experience deploying and updating a recommendation model on Kubernetes (Minikube) with CI/CD via GitHub Actions, plus prior SDE experience with Jenkins-based pipelines and on-prem to AWS migration work using Glue.”
Sason Torosean
Principal Data Scientist specializing in financial risk, forecasting, and applied ML
“ML/NLP practitioner and technical founder who built an AUP risk-scoring model at Bill.com using TF-IDF + SVD features with XGBoost, and previously created automated data-quality guardrails for a Global Equity Risk stacked ML model at Thomson Reuters. Recently built a RAG-based chatbot for PaymentJock’s Home Affordability Probability product using embeddings and a local vector database (FAISS/Chroma), improving answer quality through chunking rather than expensive fine-tuning.”
Director-level AI/ML Technology Leader specializing in healthcare and life sciences
Senior Full-Stack Engineer specializing in AI/ML developer platforms
Mid-level Data Scientist specializing in NLP, MLOps, and semiconductor manufacturing analytics
Mid-level AI/ML Engineer specializing in GPU-accelerated LLMs, RAG, and production MLOps
Mid-level Machine Learning Engineer specializing in MLOps and cloud-native ML systems
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and multi-agent systems