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Vetted Data Preprocessing Professionals
Pre-screened and vetted.
Mid-level AI Engineer specializing in LLMs, RAG, and cloud-native MLOps
Mid-level ML Engineer specializing in MLOps, data engineering, and GenAI/RAG systems
Mid-level ML Engineer specializing in FinTech risk, fraud, and GenAI RAG systems
Senior AI/ML Engineer specializing in Generative AI, LLMs, and Computer Vision
Mid-Level Full-Stack Software Engineer specializing in cloud-native microservices
Junior Software Engineer specializing in full-stack and data-driven cloud systems
Mid-level AI/ML Engineer specializing in MLOps, fraud detection, and predictive analytics
Mid-Level Full-Stack Software Engineer specializing in cloud, microservices, and AI/LLM systems
Junior Generative AI Engineer specializing in LLM fine-tuning and RAG pipelines
Mid-level AI Software Engineer specializing in LLMs and healthcare AI
Mid-level Applied AI Engineer specializing in LLMs, Prompt Engineering, and RAG
Mid-level AI Engineer specializing in LLMs, RAG, and enterprise analytics
Mid-level Full-Stack Software Engineer specializing in AI-powered document platforms
Swarag Reddy Pingili
Junior AI/ML Software Engineer specializing in LLM agents and RAG systems
“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”
Sourabh Tiwari
Mid-level Robotics Software Engineer specializing in ROS2 autonomy and computer vision
“Robotics software engineer from Bigbot who led localization and perception for an outdoor autonomous delivery robot, building ROS2/Nav2-based autonomy with EKF sensor fusion (IMU/odometry/GPS) and perception-driven dynamic costmaps. Experienced taking systems from Gazebo simulation to real-robot deployment, optimizing real-time behavior via logging-driven debugging and latency reduction, and integrating heterogeneous comms (MAVROS/MAVLink, UART/CAN, MQTT) for distributed and multi-robot setups.”
Basit Siddiqui
Senior Frontend Lead specializing in ed-tech platforms and gamified learning
“Frontend lead with ~6 years building edtech platforms (LMS + CMS) using Svelte and React/TypeScript. Manages a 6–7 person team and owns architecture, CI/CD, and production quality practices (error boundaries, crash/downtime alerting). Has hands-on experience improving performance at scale via micro-frontends, lazy loading/code splitting, and virtualization/pagination for heavy UI screens (e.g., Bonzo game platform).”
Adithya Sharma
Mid-level AI/ML Engineer specializing in MLOps, NLP, and Generative AI
“Built and deployed a production LLM-powered text-to-SQL/document intelligence chatbot on AWS that lets non-technical business users query complex enterprise databases in plain English. Demonstrates deep practical expertise in schema-aware prompting, embeddings-based schema retrieval, SQL safety/validation guardrails, and rigorous offline/online evaluation with human-in-the-loop approvals for risky queries.”
Ronald Forte
Entry-Level Software Engineer specializing in AI APIs and RAG systems
“Junior/entry-level AI/LLM engineer who built a production-oriented RAG onboarding and knowledge assistant that ingests GitHub repos and internal sources (e.g., Confluence/Jira) using ChromaDB, with reliability features like retrieval fallbacks, retries, caching, and monitoring. Currently implementing a LangGraph-based multi-agent workflow with intent routing and Pydantic/Magentic-validated structured outputs, plus CI/CD offline evals and online metrics (Grafana/Prometheus) to improve predictability and reliability.”
Ainesh Gunturu
Junior Data Analyst specializing in marketing analytics and machine learning
“Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.”