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Vetted Machine Learning Engineers in Illinois
Pre-screened and vetted in Illinois.
Yishi Wang
Junior Machine Learning & Data Science professional specializing in LLMs and analytics
“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”
Arun Yalamati
Mid-level AI & Machine Learning Engineer specializing in production ML and LLM applications
Yuvadeep Kolakari
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML services
Vaibhav Bhandari
Mid-level Software Engineer specializing in backend systems and LLM applications
Prudhvi Angirekula
Mid-level AI Engineer specializing in LLM agents, RAG, and enterprise GenAI
Miguel Saldana
Senior AI/ML Engineer specializing in GenAI, MLOps, and healthcare analytics
Evan Z
Junior Software Engineer specializing in video streaming and processing systems
“Software engineering intern at China Telecom who built and continuously evolved a real-time transaction platform ("Smart Tangerine") focused on strong consistency and peak-hour concurrency. Implemented microservices with Redis and RabbitMQ to decouple heavy processing and cut latency (~80ms to ~30ms), and led a zero-downtime migration from a monolith using strangler pattern, dual-write, and traffic shadowing.”
Janvitha Mandyam
Mid-level GenAI/ML Engineer specializing in LLM applications and RAG systems
“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”
SAIRAM KOVA
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and risk/fraud analytics
ASHRITHA KANDAKATLA
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps
chen zhu
Junior Full-Stack Software Engineer specializing in video streaming and ML pipelines
sahithi sane
Senior Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Omkar Shewale
Mid-level Machine Learning Engineer specializing in LLM inference optimization and MLOps
Chen Zhu
Junior Machine Learning Engineer specializing in LLM and multimodal systems
Siddhant Sarnobat
Mid-level Machine Learning Engineer specializing in LLMs and financial RAG systems
Vinay Yelalachinnolla
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and agentic RAG systems
Bindu Pathlavath
Mid-level AI/ML Engineer specializing in risk analytics and MLOps on AWS
Vamsi Koppala
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”
Vigneshwaran Moorthi
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and Clinical AI
“Built and productionized a HIPAA-compliant LLM+RAG Clinical AI assistant at Optum, fine-tuning GPT/LLaMA on de-identified patient notes and integrating FAISS/Pinecone for sub-second retrieval; reported to cut diagnosis time by ~20 minutes per case. Experienced in orchestrating ML pipelines (Airflow, AWS Step Functions, Azure Data Factory) and in reliability techniques for LLM systems (grounding, citations, confidence filters, monitoring) while partnering closely with clinicians and compliance teams.”