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Vetted scikit-learn Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level Full-Stack Software Engineer specializing in FinTech analytics and security
Mid-level Python Backend Developer specializing in cloud-native microservices and AI/ML platforms
Junior AI/ML Software Engineer specializing in NLP, LLM evaluation, and recommendation systems
Senior Full-Stack Software Engineer specializing in FinTech payments and risk systems
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Mid-level Software Engineer specializing in backend APIs, data pipelines, and cloud microservices
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Senior AI/ML & Data Scientist specializing in NLP, knowledge graphs, and semantic search
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Senior Software Engineer specializing in AI for Healthcare and Enterprise SaaS
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.”
Noelle Keto
Intern/Student Software Engineer specializing in full-stack development, AI/ML, and quantitative finance
“Software engineering intern who built an internal AI-agent automation using the Gemini API to reduce manual CRM data entry, iterating prompts closely with analysts to address precision concerns. Also worked on a medical image-diagnostics LLM project involving fine-tuning and benchmarking multiple model approaches, and has quant/sales-trading experience building automated pricers for complex options and persuading sales teams to adopt them with ROI-focused metrics.”
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”
Tanisha Gupta
Intern Computer Vision/Perception Engineer specializing in synthetic data and 3D/4D world modeling
“Embodied AI/robotics-focused ML engineer who built a real-time assistive Braille device by coupling transformer OCR with an Arduino-controlled electromechanical Braille cell, solving tight latency and hardware-integration constraints. Has recent work on geometry-grounded world models and a real-time 4D reconstruction foundation model (Any4D), and delivered measurable impact at Voxel AI by building a scalable headless simulation + synthetic data pipeline that improved a safety-critical algorithm’s recall by ~16%.”