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Vetted Deep Learning Professionals
Pre-screened and vetted.
Junior Computer Vision & ML Researcher specializing in 3D perception and generative models
Mid-level AI Engineer specializing in Computer Vision, NLP, and Generative AI
Senior Backend/Cloud Engineer specializing in Python, AWS, and web platforms
Mid-level Robotics Software Engineer specializing in autonomous navigation and motion planning
Mid-Level Software Engineer specializing in distributed systems and AI/ML
Mid-level Data Analyst/Data Engineer specializing in machine learning and NLP
Senior Robotics Engineer specializing in human-robot interaction and teleoperation
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 Software Engineer specializing in cloud-deployed predictive models
Mid-Level Software Engineer specializing in full-stack and cloud-native systems
Senior Robotics & Control Systems Engineer specializing in SLAM, motion planning, and data-driven control
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
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.”
Adithya Gururaj
Junior AI Engineer specializing in agentic AI, RAG, and voice/telephony systems
“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”
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.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and Azure
“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”