Pre-screened and vetted in Illinois.
Junior AI Engineer specializing in LLM systems, RAG pipelines, and cloud microservices
Intern Machine Learning & Data Science Engineer specializing in LLMs and RAG systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps
Mid-level AI Engineer specializing in developer productivity and API security
Junior Machine Learning Engineer specializing in computer vision and applied statistics
Mid-level Machine Learning Engineer specializing in LLM inference optimization and MLOps
Junior Machine Learning Engineer specializing in LLM and multimodal systems
Mid-level Machine Learning Engineer specializing in LLMs and financial RAG systems
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and agentic RAG systems
Mid-level AI/ML Engineer specializing in risk analytics and MLOps on AWS
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.”
Mid-level AI/ML Engineer specializing in GenAI, RAG, and enterprise ML systems
“ML/AI engineer with hands-on experience at Morgan Stanley building production fraud detection and enterprise RAG systems. Stands out for owning systems end-to-end—from experimentation and deployment to monitoring and iteration—and for delivering measurable impact, including an 18% reduction in fraud false positives, 40% lower inference latency, and internal tooling that reduced model deployment time from days to hours.”
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.”
Junior Machine Learning Researcher specializing in AI agents and materials modeling
“Built and shipped a production browser automation LLM agent with a structured 4-stage workflow (plan/browse/extract/verify), emphasizing reliability via schema validation (Pydantic), constrained tool use, and contextual retry loops. Reports ~60% accuracy on the WebArena benchmark and monitors runs via console output and the Agno framework GUI, prioritizing accuracy over speed.”
Senior Machine Learning Engineer specializing in MLOps and NLP/GenAI
“Built a production LLM-agent framework for a startup that performs daily financial/trading analysis by combining live market data with internal tools, including a centralized memory module to prevent context drift and reduce hallucinations. Also implemented an Airflow-orchestrated retail price forecasting pipeline deployed to AWS endpoints, scaling parallel workloads via Kubernetes Executor and validating systems with rigorous functional + LLM-specific metrics and cross-team collaboration.”
Senior Machine Learning Engineer specializing in conversational AI and healthcare ML
“ML/AI engineer focused on taking LLM products from experiment to production, with hands-on ownership of a RAG-based customer support system that improved response quality by 35% and cut latency by 30%. Stands out for combining product impact with production rigor across retrieval tuning, safety guardrails, monitoring, and reusable Python/FastAPI services that accelerated adoption across teams.”
Mid-level AI/ML Engineer specializing in Generative AI and NLP
“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.”
Mid-level AI/ML Engineer specializing in risk modeling, NLP, and generative AI (RAG/LLMs)