Role overview
**Azure Machine Learning Engineer**
**Salary Range: $100k - $150k (No Bonus)**
**100% Remote - US Based**
We are seeking an experienced Azure Machine Learning Engineer to join an award-winning, hyper-growth firm recognized multiple times as one of the best places to work. This role sits at the intersection of data science, AI operations, and cloud engineering, focusing on designing, deploying, and managing scalable machine learning systems in Azure.
You’ll work closely with data engineers, AI researchers, and DevOps professionals to operationalize machine learning pipelines, build retrieval-augmented generation (RAG) architectures, and streamline MLOps workflows using Azure ML, MLflow, and Azure DevOps.
**What You’ll Do**
**Machine Learning Engineering & MLOps**
* Design, build, and maintain production-grade ML pipelines using Azure Machine Learning, MLflow, and Azure DevOps.
* Operationalize AI models from experimentation through deployment, monitoring, and lifecycle management.
* Implement CI/CD for ML (MLOps) including automated retraining, testing, and deployment.
* Develop and manage feature stores, model registries, and automated evaluation pipelines.
* Collaborate with data scientists to translate research notebooks into scalable, production-ready systems.
**AI Systems & RAG (Retrieval-Augmented Generation)**
* Build and maintain RAG pipelines integrating Azure Cognitive Search, vector databases, and LLM frameworks (LangChain, Semantic Kernel).
* Optimize LLM performance, retrieval accuracy, and inference scalability within Azure environments.
* Implement observability and monitoring of AI APIs, inference endpoints, and data flows.
* Experiment with prompt engineering, caching strategies, and model tuning for enterprise-grade applications.
**Azure Cloud Engineering**
* Architect and maintain Azure-based infrastructure for ML workloads (IaaS, PaaS, containers, and serverless).
* Leverage Terraform, Bicep, or ARM templates to automate provisioning and ensure compliance with cloud governance policies.
* Integrate Azure services such as Key Vault, App Insights, and Container Apps into ML systems.
* Implement cost optimization and scaling strategies for compute and storage resources.
**Security & Compliance**
* Enforce secure data access and model governance aligned with SOC 2, HIPAA, or FedRAMP standards.
* Apply AI-specific security practices such as data masking, prompt injection mitigation, and API authentication.
* Work closely with cloud security teams to manage access control, encryption, and audit trails for AI services.
**What You’ll Bring**
* Bachelor’s degree in Computer Science, Data Science, or a related technical field (Master’s preferred).
* 5+ years of experience in Machine Learning Engineering, Cloud AI, or MLOps roles.
* Proven experience with Azure Machine Learning, MLflow, and Azure DevOps CI/CD.
* Proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
* Strong knowledge of Azure architecture, including networking, compute, and storage services.
* Experience building or maintaining vector databases (e.g., Azure AI Search, Pinecone, Chroma).
* Familiarity with containerization and orchestration (Docker, Kubernetes, AKS).
* Understanding of RAG architectures, LLM integration, and prompt optimization.
* Excellent communication skills, documentation discipline, and collaborative mindset.
* Curiosity to explore new approaches in AI, automation, and generative intelligence.
**Nice to Have**
* Experience contributing to AI strategy, governance, or ethical AI initiatives.
* Certifications in Azure AI Engineer Associate, Azure Solutions Architect, or MLOps Specialist.
* Familiarity with DataBricks, Synapse Analytics, or Azure Cognitive Services.
This is a unique opportunity to join a forward-thinking AI engineering team that’s defining the next generation of intelligent cloud systems.