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# AI Data Scientist / Prompt Engineer

- **Company:** Unisoft Technology Inc
- **Location:** Linthicum Heights, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Data Mining, R (Programming Language), Python (Programming Language), Machine Learning, Natural Language Processing, SQL Databases, Unstructured Data, Workflow Management Systems, Large Language Models, Scikit Learn, Information Technology, GPT, Data Pipelines
- **Published:** June 22, 2026
- **Apply:** https://www.dice.com/job-detail/e532cbdf-4b27-4492-968d-155bca831c3a

## About the Role

* Minimum 3 years of data science experience.
 * Strong background in statistical analysis and ML; proficiency in SQL, Python, R, or similar.
 * Experience with ML libraries/frameworks and methods such as regression, clustering, and classification.
 * 2+ years hands-on with models such as GPT-4, Claude, Llama, Gemini or similar, and their APIs.
 * Expert-level with orchestration tools such as LangChain, LlamaIndex, or Haystack.
 * Experience with vector databases (Pinecone, Weaviate, Milvus, pgvector) and synthetic / instruction-tuning datasets.
 * Preferred: Experience in regulated/public-sector environments with PII/PHI and ethical AI standards.

Education

 * Master's or PhD in computer science, statistics, mathematics, economics, or related field.
 * Three years of equivalent related experience may substitute for a Bachelor's degree.

## Description

* Collect, process, and analyze structured and unstructured data using data mining, modeling, NLP, and ML techniques.
 * Develop predictive models and algorithms and design automated data pipelines and workflows.
 * Build dashboards, reports, and visualizations; collaborate with multiple teams to refine requirements.
 * Implement AI governance and safety guardrails to reduce hallucinations, bias, and security risks (e.g., prompt injection).
 * Develop LLM evaluation benchmarks with automated metrics and human-in-the-loop feedback.
 * Identify parameter-efficient fine-tuning (PEFT/LoRA) opportunities for state government datasets.


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