AI / Machine Learning Engineer Job at Signify Technology (Los Angeles)

Job Responsibility

Design and implement AI/ML solutions with Azure Machine Learning, Azure AI Foundry (AI Studio), OpenAI on Azure, and Copilot Studio
Build, fine-tune, and evaluate LLM-based applications
Develop and maintain Python pipelines (ETL/ELT) and ML models
Deliver demand prediction, sales forecasting, and operational planning models
Integrate AI into Power Platform solutions and line-of-business apps
Build task-oriented AI agents and automation workflows
Design context management patterns for AI systems
Design scalable data products on Fabric/Databricks/Synapse
Build CI/CD for models and prompts
Implement telemetry and responsible AI evaluations
Create runbooks, model cards, data contracts, and playbooks

5+ years in software/data engineering or machine learning
2+ years building AI/ML or LLM-based systems in production environments
Hands-on experience with Azure AI services, Azure AI Foundry, Azure Machine Learning, OpenAI on Azure, and Copilot Studio
Working knowledge of Lakehouse architecture and tools such as Microsoft Fabric, Databricks, and/or Azure Synapse
Proficiency with Git, Azure DevOps (or GitHub), Agile methods (e.g., Jira), and CI/CD pipelines for analytical solutions
Proven experience building autonomous agents or AI-driven workflows with safety and observability
Expertise in prompt engineering, fine-tuning, RAG, and evaluation frameworks
Comprehensive understanding of the ML lifecycle from experimentation through deployment, monitoring, and continuous improvement

Strong Python development skills and experience with machine learning and LLM frameworks such as PyTorch, TensorFlow, or Hugging Face
Experience building LLM-powered applications, including prompt engineering, RAG pipelines, and evaluation frameworks
Familiarity with vector embeddings and semantic search using technologies such as Azure OpenAI or Azure AI Search
Strong understanding of forecasting and time-series modeling techniques
Experience building data products and pipelines using Fabric, Databricks, Synapse, or similar lakehouse architectures
Experience integrating AI systems with enterprise data sources and APIs
Experience implementing MLOps practices on Azure, including model registry, CI/CD pipelines, automated retraining, and monitoring
Familiarity with Azure DevOps or GitHub Actions for AI/ML lifecycle automation
Knowledge of data privacy, security, and responsible AI principles