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# AI Engineer - INTL India

- **Company:** Insight Global
- **Location:** Orlando, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Code Review, Continuous Integration, High-Level Architecture, Python (Programming Language), Machine Learning, Natural Language Processing, Tensorflow, Azure Machine Learning, Software Engineering, Cloud Platform System, Pytorch, Large Language Models, Deep Learning, Model Validation, Generative AI, Machine Learning Operations, Databricks
- **Published:** May 24, 2026
- **Apply:** https://www.juju.com/job/00000000g24xuv

## About the Role

* 5-8+ years of hands-on experience in AI/ML engineering or applied machine learning.

 * Expert Python and strong software engineering practices (testing, packaging, code review).

 * Expertise with NLP/LLMs.

 * Experience deploying models to production (batch and/or real-time) in cloud environments (Azure preferred).

 * Strong understanding of model evaluation, monitoring concepts, and data/feature quality.

Desired Skills:

 * Experience with Azure Machine Learning, Databricks, MLflow, and vector databases.

 * Experience building retrieval-augmented generation (RAG) systems and prompt evaluation.

 * Strong communication skills with ability to explain technical concepts to business stakeholders.

## Description

* Design, build, and deploy AI/ML solutions leveraging LLMs, NLP, forecasting, personalization, and generative AI use cases.

 * Translate business problems into technical designs, model architectures, and scalable services.

Model Development & Optimization:

 * Implement and train deep learning models (e.g., transformers, sequence models) using PyTorch/TensorFlow.

 * Run experiments, hyperparameter tuning, and evaluation; document results and recommendations.

Production Integration:

 * Work with ML Ops to operationalize models through CI/CD, model registries, and repeatable training pipelines.

 * Develop APIs and batch scoring jobs; ensure performance, latency, and cost targets.

Governance & Responsible AI:

 * Apply responsible AI practices: bias assessment, explainability where needed, and model risk documentation.

 * Ensure compliance with data privacy, security, and internal standards.


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