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Vetted Logistic Regression Professionals
Pre-screened and vetted.
Intern AI Software Engineer specializing in backend systems and data platforms
Mid-level ML Engineer specializing in MLOps, data engineering, and GenAI/RAG systems
Mid-level AI/ML Engineer specializing in LLM agents, search/recommendation, and MLOps
Mid-level ML Engineer specializing in FinTech risk, fraud, and GenAI RAG systems
Junior Data Scientist specializing in machine learning and analytics
Mid-level GenAI Engineer specializing in agentic workflows, RAG, and LLM orchestration
Mid-level Data Scientist specializing in Healthcare ML and Generative AI
Junior Generative AI Engineer specializing in LLM fine-tuning and RAG pipelines
Junior Software Engineer specializing in QA automation and full-stack web development
Mid-level Applied AI Engineer specializing in LLMs, Prompt Engineering, and RAG
Junior Data Scientist / AI Engineer specializing in analytics, ML, and MLOps
Mid-level AI Engineer specializing in LLM automation and RAG systems
Mid-level AI/ML Engineer specializing in cloud AI, MLOps, and NLP
Mid-level Data Analyst specializing in marketing analytics and machine learning
Mid-level Data Engineer specializing in cloud data platforms and real-time pipelines
Vikesh Patel
Senior AI/ML Engineer & Data Scientist specializing in LLMs, RAG, and MLOps
“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”
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.”
Nikhitha Todeti
Mid-level AI Engineer specializing in ML, LLM applications, and data automation
“Data/ML practitioner who has built a production RAG-based knowledge assistant integrated into Microsoft 365/internal dashboards to help employees query internal documents in plain English. Experienced orchestrating and hardening ETL pipelines with Airflow and Azure Data Factory (validation, retries, monitoring) and running end-to-end model evaluation and production performance tracking via Power BI.”
Ttanvi Tummapudi
Junior Data Scientist specializing in machine learning, predictive modeling, and applied AI research
“Data scientist/researcher who has built two multimodal LLM systems: an AI-assisted medical triage pipeline using GPT-4o vision + RAG with confidence-scored red/yellow/green outputs, and a master’s project on multimodal cyberthreat detection combining multiple models and using TinyLlama to generate human-readable risk reports. Also partnered with business analysts at Sanvar Technologies to deliver a churn prediction pipeline and Tableau dashboard for decision-making.”
Sree Manasa Vuppu
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG
“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”
Aman Kumar Mushnam
Intern Full-Stack/Backend Engineer specializing in cloud-native APIs and event-driven systems
“Backend-focused engineer who built an academic AI voice assistant with a Python microservice-style backend (speech recognition, spaCy-based NLP, and Kafka-driven automation) optimized to sub-500ms latency. Also has Sodexo internship experience deploying containerized services across Kubernetes/AWS ECS/Azure using ArgoCD GitOps, including solving config drift and secret-management challenges and supporting cloud-to-on-prem migrations with blue-green rollouts.”