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# AI/ML Engineer

- **Company:** Accede Solutions Inc
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Encodings, Continuous Integration, Monitoring of Systems, Python (Programming Language), Machine Learning, Performance Tuning, Cloud Services, Search Technologies, Large Language Models, IT Architecture, Fastapi, Containerization, AI Platforms, Machine Learning Operations, Virtual Agents, Data Pipelines
- **Published:** August 7, 2026
- **Apply:** https://www.dice.com/job-detail/70913486-dd79-4c97-bf31-fb2ca26bbe1c

## About the Role

* 5+ years of experience in ML Engineering, Applied AI, or a related field.
 * Strong hands-on Python development experience.
 * Proven experience deploying ML models into production.
 * Hands-on experience building LLM applications, RAG systems, and agentic workflows.
 * Experience with vector databases, embeddings, and semantic/vector search.
 * Experience with LangChain, LlamaIndex, or similar frameworks.
 * Experience with FastAPI, MLflow, or similar model-serving/API technologies.
 * Strong knowledge of CI/CD, containerization, MLOps, and cloud deployments.
 * Understanding of ML and LLM evaluation, monitoring, and performance optimization.

Preferred

Experience with AI platforms, foundation-model fine-tuning, LLM evaluation/monitoring tools, regulated environments, and large-scale inference optimization is a plus.

## Description

We are seeking an experienced Sr. AI/ML Engineer to design, build, and deploy scalable AI/ML solutions, including traditional machine learning models and LLM-powered applications. The ideal candidate will have strong hands-on experience with RAG pipelines, agentic AI workflows, Python, vector databases, MLOps, and cloud deployments., * Design, develop, and deploy production-ready AI/ML and LLM solutions.

 * Build RAG pipelines, embedding workflows, vector search, and agentic AI systems.
 * Develop and optimize LLM orchestration and prompt strategies.
 * Deploy and serve models using tools such as FastAPI and MLflow.
 * Build scalable MLOps, CI/CD, monitoring, and model lifecycle workflows.
 * Optimize AI systems for performance, scalability, latency, and cost.
 * Develop reliable data pipelines with proper validation and reproducibility.
 * Evaluate ML/LLM performance and implement appropriate guardrails and monitoring.
 * Collaborate with cross-functional teams and contribute to AI architecture decisions.


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