.png){kind=logo}
Vetted Unsupervised Learning Professionals
Pre-screened and vetted.
Dyuti Vartak
Junior Data Scientist/Data Engineer specializing in ML pipelines and analytics
“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”
Harsha Sikha
Mid-level AI/ML Engineer specializing in Generative AI and data engineering
“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”
Palaniappan Yeagappan
Junior Robotics Engineer specializing in autonomous driving and SLAM
“Robotics software engineer focused on real-time state estimation and perception pipelines, with hands-on C++/ROS work improving LiDAR+IMU odometry stability via an iterative EKF and careful timing/synchronization fixes. Has integrated LIO-SAM, built multi-robot communication bridges (ROS + custom UDP with heartbeat/fallback), and uses Gazebo + Docker for repeatable testing, backed by CI/CD experience maintaining Azure DevOps pipelines at Cognizant.”
Ajay Kumar Devireddy
Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps
“ML/AI engineer with healthcare payer experience (Signal Healthcare, Cigna) who has shipped production fraud/claims prediction systems using Python/TensorFlow and exposed them via FastAPI/Flask microservices integrated with EHR and Salesforce. Emphasizes operational reliability and trust—Airflow-orchestrated pipelines with data quality gates plus SHAP-based interpretability, A/B testing, and drift/debug workflows—backed by reported outcomes of 22% lower false payouts and 17% higher model accuracy.”
Fatemeh Taghvaei
Junior AI/ML Engineer and Instructor specializing in deep learning, computer vision, and NLP
“Computer-vision practitioner and educator who built a real-time license plate recognition system (OpenCV/Python + KNN) optimized to run on a Raspberry Pi with camera integration. Also designs hands-on deep learning coursework, incorporating recent transformer-based vision research (Vision Transformers) into practical labs on real datasets.”
Mid-level AI/ML Engineer specializing in NLP, MLOps, and production ML systems
Entry-level Machine Learning Engineer specializing in multimodal AI and LLM systems
Senior Software Engineer/Architect specializing in robotics vision and embedded systems
Mid-level Data Scientist specializing in NLP, recommender systems, and cloud ML
Mid-level AI Engineer specializing in ML platforms, recommender systems, and GenAI/RAG
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and Generative AI
Mid-level Machine Learning Engineer specializing in healthcare analytics and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, NLP, and computer vision
Mid-level AI/ML Engineer specializing in Generative AI and Financial Services
Mid-level GenAI Engineer specializing in LLM fine-tuning, RAG, and MLOps
Mid-level Data Scientist specializing in ML, NLP/LLMs, and fraud & risk analytics
Mid-level Machine Learning Engineer specializing in healthcare NLP and financial risk modeling
Senior Data Scientist / ML Engineer specializing in GenAI, NLP, and compliant healthcare ML
Mid-level AI/ML Engineer specializing in fraud detection and NLP/LLM solutions
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and agentic AI