Principal AI Engineer

Confiz•Phoenix, AZ

1d•Onsite

About The Position

Confiz 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.

Requirements

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.
Programming: Python (mandatory), plus exposure to SQL, and at least one full-stack language (JavaScript/TypeScript, C#, or Java).
Architecture: Proven experience designing scalable, distributed systems; solid grasp of system design principles, security, and DevOps/CI-CD practices.
Strong stakeholder communication skills — ability to translate technical architecture into business value for both technical and non-technical audiences.

Nice To Haves

Experience with vector databases (Pinecone, Weaviate, Snowflake Cortex).
Exposure to containerization (Docker/Kubernetes) and infrastructure-as-code (Terraform/Bicep).
Prior experience in a client-facing or pre-sales/solutioning capacity.
Certifications in Azure/AWS AI or Databricks (Databricks Certified Data Engineer/ML Associate).

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 requirements 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).