Vetted Streamlit Professionals

“Built and deployed an LLM-powered automation pipeline that ingests voice and documents, transcribes/extracts key information into structured data, and routes it through backend workflows using Python/FastAPI. Uses n8n to orchestrate multi-step AI processes with validation, retries, and monitoring, and iterates with stakeholders via rapid demos to refine changing requirements.”

“Backend/ML engineer with deep fintech and marketplace experience: built a real-time financial analytics + algorithmic trading platform (Python/Postgres/Kafka/Redis) and drove major DB performance wins (10x faster analytics; sub-10ms response consistency). Also shipped an end-to-end ML recruitment matching platform (scraping/ETL/modeling/Django deployment) with reported 92% matching accuracy, and emphasizes production reliability via monitoring, blue-green deploys, and robust workflow error handling.”

Sarang Pratham

Screened

Junior Software Engineer specializing in data engineering and GenAI

Kundapur, India3y exp

“Built and deployed a production LLM-powered recruitment chatbot that automates key recruiting steps (sourcing, candidate engagement, screening). Strong in agent orchestration with LangGraph, including guided graph-based workflows, context-aware routing, and reliability measures like clarifying steps plus human-in-the-loop evaluation.”

“Analytics and automation candidate with experience delivering data and AI solutions for major public-sector and enterprise clients including STC, Abu Dhabi Ports, and the Ministry of Culture. They combine SQL, Python OCR pipelines, dashboards, and cloud LLMs to turn messy unstructured data into scalable workflows, with reported impact including 70% faster document processing and 80-85% reduction in manual resume screening.”

“AI engineer with hands-on experience shipping production systems across semantic search, RAG/LLM applications, and computer vision. Built a personalized e-commerce search platform with measurable relevance and latency gains, and deployed grounded GenAI chat systems that significantly reduced hallucinations while lowering support burden. Also brings edge-deployment experience in monocular depth estimation and 3D reconstruction, suggesting strong breadth across modern applied AI.”

Richa Chanpuriya

Screened

Mid-level Backend/Agentic AI Engineer specializing in GenAI automation and RAG systems

Remote, India3y exp

“Built and shipped a production AI-driven privacy automation system that autonomously navigates data broker sites to submit opt-out/data deletion requests end-to-end, including robust CAPTCHA detection/solving (e.g., reCAPTCHA/hCaptcha/Cloudflare) via 2Captcha. Experienced in orchestrating stateful LLM agent workflows with LangGraph and hardening them for production with strict state management, retries/fallbacks, validation layers, and database-backed observability/audit logs, collaborating closely with legal/compliance stakeholders.”

Manas Kumar Sherla

Screened

Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI

Charleston, IL2y exp

“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”

Mohammad Al-Hudban

Screened

Intern AI Engineer & Data Scientist specializing in GenAI, LLMs, and RAG

Leoben, Austria0y exp

“Currently working at CBS Lab in Austria, where they implemented/replicated the "Open World Grasping" research pipeline end-to-end. Built a ROS-based RGB-D perception-to-action system using SAM 2.1 segmentation and MoveIt motion planning to generate grasp poses and execute pick-and-place/sorting with a robotic arm.”

Kumar Manik

Screened

Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

0y exp

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”