Principal AI Engineer

Job summary

Con*** is seeking a Principal AI Engineer in the Phoenix AZ market, who can design and drive end-to-end AI-powered solutions from data engineering and model architecture to full-stack integration and deployment. This role bridges data platforms, AI/ML systems, and application development, ensuring solutions are scalable, secure, and production-ready.

Qualifications

• into technical architecture.
• Design scalable, secure, and cost-optimized cloud architectures (Azure/AWS/GCP) for AI workloads.
• Conduct architecture reviews, POCs, and technical feasibility assessments for new AI initiatives.
• Mentor engineering teams on AI integration patterns, prompt engineering, RAG pipelines, and agentic workflows.
• Ensure solutions meet performance, security, and compliance standards (data privacy, responsible AI practices).
• 10+ years in software/solution architecture, with 4+ years specifically in AI/ML architecture.
• AI/ML: Strong understanding of LLMs, RAG architectures, agentic AI systems, prompt engineering, model fine-tuning, and MLOps.
• Databricks: Hands-on experience with Databricks Lakehouse (Delta Lake, Unity Catalog, MLflow, Databricks Workflows), Spark-based data processing.
• Full-Stack Development: Working knowledge of front-end (React/Angular) and back-end (Node.js/.
• NET/Python) development, API design (REST/GraphQL), and microservices architecture.
• Cloud Platforms: Experience with Azure (AI Foundry, Cognitive Services) and/or AWS/GCP AI & data services.
• Data Engineering: Familiarity with ETL/ELT pipelines, data modeling, and data governance.

Responsibilities

• Design and architect AI/ML solutions, including LLM-based applications, agentic systems, and predictive models, aligned with business objectives.
• Define data architecture and pipelines using Databricks (Delta Lake, Unity Catalog, MLflow) for large-scale data processing and model training/serving.
• Architect full-stack solutions that integrate AI models into web/enterprise applications — covering front-end, back-end APIs, and cloud infrastructure.
• Evaluate and select appropriate AI frameworks, LLM providers (OpenAI, Anthropic, Azure AI Foundry, etc.), and vector databases for use-case fit.
• Establish best practices for model lifecycle management: versioning, monitoring, retraining, and governance.
• Collaborate with data engineers, ML engineers, full-stack developers, and product owners to translate business

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