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# AI/ML developer

- **Company:** Q1 Technologies, Inc.
- **Location:** Aurora, IL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Continuous Integration, Information Engineering, Information Leak Prevention, DevOps, Distributed Computing Environment, Python (Programming Language), Machine Learning, Natural Language Processing, Tensorflow, Azure Machine Learning, Software Engineering, Management of Software Versions, Feature Store, Scripting, Pytorch, Retrieval-Augmented Generation, Large Language Models, Generative AI, Scikit Learn, Kubernetes, Deployment Automation, Data Analytics, Machine Learning Operations, Api Design, Restful APIs, GPT, Docker, Unsupervised Learning, Databricks
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-ml-developer-aurora-il--5b344e4e-54c0-4222-80c7-a6ea91adc0bb

## About the Role

Artificial Intelligence (AI), Artificial Intelligence (AI), Data Science, Prototyping, Automation, Machine Tool, Incident Response, Software Engineering, Python Programming/Scripting Language, Application Programming Interface (API), 5+ years software engineering with 2+ years shipping ML models to production.

 - Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).

 - Experience with containers and orchestration (Docker/Kubernetes) and API development.

 - Understanding of ML system design (data leakage, training-serving skew, drift).

 - CI/CD and DevOps practices applied to ML workloads (MLOps).

Preferred / Nice to Have

 - Experience with feature stores, model registries, and model monitoring stacks.

 - GPU optimization and distributed training experience.

 - Experience with responsible AI toolkits and compliance requirements.

Core Skills (from POD sheet)

Python, TensorFlow, PyTorch, Docker, REST APIs

Role Descriptions: RoleAIML EngineerRequired Skills 3 years of experience applying NLP (transformers| gpt| etc) in a production setting using large amounts of data 2-3 years of experience applying LLMs (RAG| vector db| embeddings) to solve real world problems 2-3 years of experience in Python using common data science libraries such as scikit-learn and Databricks Experience applying data science techniques in a commercial enterprise| including supervised| unsupervised learning| NLP| time-series forecasting and other statistical analyses Experience building APIs for the models Strong written verbal communication skills including the ability to present detailed analyses to a broad audience Organized self-starter| with drive and commitment able to work with little supervision Strong analytical| quantitative| problem-solving| and critical thinking skills Experience using vision and speech models in GenAI
Essential Skills: RoleAIML EngineerRequired Skills 3 years of experience applying NLP (transformers| gpt| etc) in a production setting using large amounts of data 2-3 years of experience applying LLMs (RAG| vector db| embeddings) to solve real world problems 2-3 years of experience in Python using common data science libraries such as scikit-learn and Databricks Experience applying data science techniques in a commercial enterprise| including supervised| unsupervised learning| NLP| time-series forecasting and other statistical analyses Experience building APIs for the models Strong written verbal communication skills including the ability to present detailed analyses to a broad audience Organized self-starter| with drive and commitment able to work with little supervision Strong analytical| quantitative| problem-solving| and critical thinking skills Experience using vision and speech models in GenAI
Desirable Skills:
Keyword:
Skills: Digital : Machine Learning~Digital : Natural Language Processing (NLP)~AI & Gen AI - Products & Tools
Experience Required: 4-6

## Description

Translate data science prototypes into production-grade ML services and pipelines.

 - Build training and inference code with reproducibility, versioning, and automated testing.

 - Implement scalable model serving (online/offline), batching, and latency/throughput optimization.

 - Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).

 - Collaborate with Data Engineering on feature pipelines and data contracts.

 - Own production health: drift detection, performance regression, rollback strategies, and incident response.