Top Machine Learning Jobs in Peoria, IL

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, integrate and fine-tune various LLMs, and deploy containerized FastAPI services with monitoring, CI/CD, and performance/cost optimizations.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Design, develop, and deploy end-to-end machine learning models using statistical and ML algorithms. Prepare and enrich data, implement scalable PySpark/Databricks solutions, and collaborate with product and engineering to create personalized customer experiences and actionable insights.

Design, deploy, and optimize cloud-native ML systems on AWS, integrate foundation models (Bedrock/Anthropic), build and monitor SageMaker pipelines and data pipelines, detect drift, extend ML infra and libraries, and collaborate cross-functionally to deliver secure, cost-efficient production ML services.

Design, develop, and maintain scalable ML and AI platform infrastructure for model training, deployment, feature engineering, candidate generation, agent deployment, and observability. Collaborate with data scientists and engineers, support platform operations, drive improvements, codify best practices, and influence platform investments to enable production ML systems at scale.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Lead research and intelligence direction for a high-frequency AI assistant: set model and systems strategy (memory, reasoning, planning), choose architectures, define real-world evaluation and safety frameworks, drive frontier techniques, and partner with product and engineering to ship robust, production ML systems.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Design, train and validate supervised ML models on multi-billion claim records to score fraud risk; engineer features in-warehouse; deploy and monitor models in production; provide human-readable rationale and claim-level evidence; collaborate with subject-matter experts and stakeholders to ensure actionable, explainable outputs.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

Build, deploy, and maintain production-grade ML systems across the full model lifecycle. Translate business goals into ML solutions, monitor model performance and drift, perform analyses and experiments, and collaborate with data engineers, product, and domain experts to improve risk assessment and underwriting outcomes.

Design, train, evaluate, deploy, and monitor machine learning models. Prepare and analyze large datasets, improve model performance through experiments, and collaborate with software engineers to integrate ML solutions into production while staying current with ML techniques.

Design, build, deploy, and operate AI/ML capabilities in AWS GovCloud using FedRAMP-authorized services. Responsibilities include data preparation, model development, evaluation, deployment, monitoring, RAG implementations, embedding/vector search integration, Python development of production services, containerized deployments, CI/CD, and collaboration with engineering and cybersecurity teams to meet mission and compliance requirements.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

The role involves developing and deploying machine learning solutions, optimizing models, and collaborating with cross-functional teams. Responsibilities include cloud architecture, algorithm engineering, and infrastructure best practices.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Own end-to-end production ML systems for search, recommendations, and ranking: architecture, training pipelines, inference, deployment, monitoring, drift detection, and retraining. Partner with data, product, and backend teams to ship reliable, low-latency ML-driven product experiences and set standards for ML infrastructure and model health.

Lead and grow a team of ML engineers building production ML systems for Fetch's Ad Platform. Translate product strategy into roadmaps, guide design and implementation of models for ranking, targeting, bidding and forecasting, set metrics and experimentation, enforce architectural and operational standards, manage trade-offs across performance and reliability, and drive cross-functional alignment to improve advertiser and member outcomes.