AI Summary
Design, develop, and deploy AI/ML models using Microsoft Foundry and Azure AI. Requires 7+ years of experience in machine learning, deep learning, or AI engineering. Strong expertise in ML/DL fundamentals and large language models.
Key Highlights
Microsoft Foundry Specialist
AI/ML Engineer
7+ years of experience in machine learning
Key Responsibilities
Design, develop, and deploy AI/ML models using Microsoft Foundry and Azure AI.
Strong expertise in ML/DL fundamentals and large language models.
Production experience deploying models at scale using Azure ML managed endpoints, Foundry model deployments, and container-based serving infrastructure (AKS, Container Apps).
Technical Skills Required
Benefits & Perks
Fully Remote
Salary range not explicitly stated
Nice to Have
Microsoft certifications: Azure AI Engineer Associate (AI-102), Azure Data Scientist Associate (DP-100), or Azure Solutions Architect Expert (AZ-305).
Experience with Foundry's latest capabilities: Agent-to-Agent (A2A) protocol, Foundry MCP Server, Model Router, and the unified azure-ai-projects SDK (v2).
Background in specific industry AI applications such as financial services, healthcare, manufacturing, or energy.
Job Description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Gtech LLC, is seeking the following. Apply via Dice today!
Role: Microsoft Foundry Specialist - AI/ML Engineer
Location: Fully Remote
Required Qualifications
- 7+ years of experience in machine learning, deep learning, or AI engineering, with at least 2 years of hands-on experience on Azure AI Foundry or Microsoft Foundry.
- Strong theoretical and applied expertise in ML/DL fundamentals: neural network architectures (transformers, CNNs, RNNs, GANs), training methodologies, optimization algorithms, regularization techniques, and model evaluation metrics.
- Deep understanding of large language models, including transformer architecture, attention mechanisms, tokenization, prompt engineering, fine-tuning (LoRA, QLoRA, full fine-tuning), and inference optimization (quantization, distillation, speculative decoding).
- Production experience deploying models at scale using Azure ML managed endpoints, Foundry model deployments, and container-based serving infrastructure (AKS, Container Apps).
- Proficiency in Python with strong experience in ML frameworks (PyTorch, TensorFlow/Keras) and AI orchestration libraries (LangChain, LangGraph, Semantic Kernel).
- Hands-on experience with RAG architectures, including vector databases, embedding models, chunking strategies, hybrid search (keyword + semantic), and reranking pipelines.
- Solid understanding of MLOps practices: CI/CD for ML pipelines, model versioning, experiment tracking, automated retraining, and drift monitoring.
- Experience implementing responsible AI practices, including fairness assessments, content filtering, toxicity detection, and model transparency reporting.
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- Microsoft certifications: Azure AI Engineer Associate (AI-102), Azure Data Scientist Associate (DP-100), or Azure Solutions Architect Expert (AZ-305).
- Experience with Foundry's latest capabilities: Agent-to-Agent (A2A) protocol, Foundry MCP Server, Model Router, and the unified azure-ai-projects SDK (v2).
- Background in specific industry AI applications such as financial services, healthcare, manufacturing, or energy.
- Experience with multi-modal AI systems (vision, speech, text) and generative media models.
- Contributions to open-source ML/AI projects or published research in ML/AI conferences or journals.
- Familiarity with competitive AI platforms (AWS SageMaker, Google Vertex AI) for comparative solutioning.
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- Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field required.
- Master's or Ph.D. in Machine Learning, Artificial Intelligence, or a related discipline preferred.
- Equivalent professional experience, publications, or open-source contributions will be considered in lieu of formal education.
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