> Markdown version of [/jobs/ext/4034334-data-science-ml-engineer](https://www.wearedevelopers.com/jobs/ext/4034334-data-science-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Science/ML Engineer

- **Company:** Stellar IT Solutions
- **Location:** Chicago, IL, United States
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Computer Vision, Python (Programming Language), Machine Learning, Natural Language Processing, LangChain, Retrieval-Augmented Generation, Large Language Models, Google Vertex AI, IT Architecture, Deep Learning, Llamaindex, Generative AI, Agentic-AI, AI Platforms, HuggingFace
- **Published:** October 30, 2026
- **Apply:** https://www.dice.com/job-detail/bb220c08-266e-4159-91d3-4e04862ebc16

## About the Role

* Strong Data Science/Machine Learning background, preferably someone who started their career in ML or Data Science before moving into architecture.
 * Target 10 15 years of experience, rather than 20+ years.
 * Hands-on experience architecting enterprise Generative AI solutions.
 * Strong knowledge of Machine Learning, NLP, or Computer Vision.
 * Experience with LLMs, RAG architectures, and Agentic AI frameworks.
 * Experience building AI POCs and transitioning successful solutions toward production.
 * Strong Python skills and familiarity with ML/Deep Learning frameworks.
 * Experience with AI technologies such as LangChain, LangGraph, LlamaIndex, vector databases, and cloud AI platforms.
 * Excellent communication, presentation, and stakeholder management skills.

Nice to Haves:

 * Experience working within an AI Center of Excellence (CoE).
 * Experience with Google Vertex AI, AWS SageMaker, or Hugging Face.
 * Experience leading enterprise AI transformation initiatives.
 * Background across NLP, Computer Vision, and Predictive Analytics.
 * Experience estimating ROI and presenting AI investment recommendations to leadership.

## Description

* Design enterprise GenAI solutions using LLMs, RAG, Agentic AI, and Machine Learning.
 * Work with business stakeholders to identify AI opportunities and translate requirements into scalable architectures.
 * Develop POCs, MVPs, and prototypes to demonstrate technical feasibility and business value.
 * Define AI architecture covering model selection, orchestration, vector databases, and deployment.
 * Collaborate with engineering teams to transition approved solutions into production.
 * Present AI solutions and architecture recommendations to technical teams and executive leadership.