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# AI Engineer

- **Company:** Goldenpick Technologies
- **Location:** Houston, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computer Vision, Cloud Computing, Databases, Information Engineering, Extract Transform Load (ETL), Data Transformation, DevOps, Distributed Systems, Github, Python (Programming Language), Machine Learning, NoSQL, NumPy, Tensorflow, Azure Machine Learning, SQL Databases, Data Streaming, Systems Integration, Unstructured Data, Feature Engineering, Pytorch, Large Language Models, Prompt Engineering, Apache Spark, Backend, Git, Pandas, Gitlab-ci, Scikit Learn, Low Latency, HuggingFace, Machine Learning Operations, Front End Software Development, Software Version Control, Data Pipelines, Docker, Jenkins, Microservices
- **Published:** July 23, 2026
- **Apply:** https://www.dice.com/job-detail/d82efa15-89c9-4f34-8dde-5357147f0a3d

## About the Role

Skills Must have

 * Programming: Python (preferred), Java, or Scala
 * ML Frameworks: TensorFlow, PyTorch, Scikit-learn
 * AI/GenAI: prompt engineering (preferred), Claude CLI / Code(preferred), LLMs(preferred) Hugging Face, OpenAI APIs
 * Data Tools (Optional): Pandas, NumPy, Spark
 * APIs & Microservices development
 * Version control (Git)
 * Cloud & DevOps
 * Experience with AWS
 * Containers: Docker, ECS/EKS
 * CI/CD pipelines (GitHub Actions Or Jenkins Or GitLab CI)
 * Data & Systems
 * Databases: SQL, NoSQL
 * Familiarity with data pipelines and ETL processes (Optional)
 * Understanding of distributed systems

## Description

* AI/ML Model Development
 * Design, develop, and optimize machine learning and deep learning models
 * Build NLP, computer vision, or predictive analytics solutions
 * Train, test, and evaluate models for accuracy, scalability, and performance
 * Fine-tune pre-trained models (e.g., LLMs, transformers) for business use cases
 * Data Engineering & Processing (Optional)
 * Collect, clean, and preprocess structured and unstructured datasets
 * Work with large-scale data pipelines and streaming data systems
 * Implement feature engineering and data transformation workflows
 * Deployment & MLOps
 * Deploy models into production using APIs, containers, or microservices
 * Work with DevOps to build CI/CD pipelines for ML workflows (MLOps)
 * Monitor model performance, drift, and reliability in production
 * Optimize latency, throughput, and cost efficiency
 * Cloud & System Integration
 * Integrate AI solutions into cloud platforms (AWS)
 * Work with services like SageMaker, Azure ML, Vertex AI, or OpenAI APIs
 * Collaborate with DevOps, backend, and frontend teams for implementation
 * Research & Innovation
 * Stay up to date with emerging AI technologies and frameworks
 * Evaluate and implement GenAI, LLMs, and prompt engineering techniques
 * Prototype and experiment with new AI-driven solutions


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