What are the responsibilities and job description for the Staff AI Engineer position at Vasion Print (Formerly PrinterLogic)?
Vasion is seeking a Staff AI Engineer who embodies our core values and is eager to join our dynamic team. We are dedicated to enabling digital transformation for everyone by providing an affordable, integrated SaaS solution that simplifies business operations. Vasion offers a flexible working environment for our 400 employees around the globe, including at our headquarters in St. George, Utah, and offices in the UK, Germany, and Lehi, Utah.
Role Overview
We are seeking a highly skilled Staff AI Engineer to lead the development of next-generation agentic AI systems that seamlessly integrate with enterprise tools, workflows, and services. This role emphasizes building robust orchestration frameworks, leveraging modern connectivity standards, and deploying scalable solutions across leading cloud AI platforms. As a senior technical leader, you will define architectural best practices, mentor engineering teams, and drive innovation in model fine-tuning, retrieval pipelines, and agentic design patterns. You will work cross-functionally to deliver transformative AI capabilities with measurable business impact, while ensuring systems are scalable, secure, and responsible.
Responsibilities
Role Overview
We are seeking a highly skilled Staff AI Engineer to lead the development of next-generation agentic AI systems that seamlessly integrate with enterprise tools, workflows, and services. This role emphasizes building robust orchestration frameworks, leveraging modern connectivity standards, and deploying scalable solutions across leading cloud AI platforms. As a senior technical leader, you will define architectural best practices, mentor engineering teams, and drive innovation in model fine-tuning, retrieval pipelines, and agentic design patterns. You will work cross-functionally to deliver transformative AI capabilities with measurable business impact, while ensuring systems are scalable, secure, and responsible.
Responsibilities
- Agentic AI Orchestration: Architect, implement, and optimize agent workflows using frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, Semantic Kernel, CrewAI, or similar platforms for multi-step reasoning, planning, and execution.
- Connectivity and Tooling: Build integrations with enterprise systems through standards such as MCP (Model Context Protocol), function calling APIs (e.g., OpenAI Assistants, Anthropic Claude), and custom APIs (FastAPI, GraphQL, gRPC).
- Cloud AI Deployment: Design, deploy, and optimize AI solutions on enterprise-grade platforms including Azure AI Foundry, AWS Bedrock, GCP Vertex AI, Databricks Mosaic AI, and Snowflake Cortex AI.
- Knowledge and Retrieval Systems: Develop retrieval-augmented generation (RAG) pipelines leveraging vector databases (Pinecone, Weaviate, Milvus, FAISS) and hybrid search/knowledge graph solutions.
- Model Lifecycle Management: Lead efforts in fine-tuning foundation models, embeddings, prompt engineering, and evaluation for domain-specific use cases.
- Infrastructure and Scalability: Collaborate with platform and DevOps teams to ensure secure, performant, and cloud-native AI deployments using Kubernetes, Docker, and workflow orchestration tools (Airflow, Prefect, Dagster, Temporal).
- Observability and Governance: Establish monitoring and evaluation frameworks using LangSmith, Weights and Biases, MLflow, Arize, or Evidently AI to ensure reliability, transparency, and compliance.
- Innovation and Research: Explore emerging trends in agentic AI, orchestration, and generative systems, applying them to improve intelligence, reliability, and efficiency.
- Mentorship and Leadership: Provide technical direction, mentor engineers and data scientists, and set engineering standards for scalable AI system development.
- Responsible AI: Advocate for and implement responsible AI practices, ensuring transparency, safety, and fairness in AI-driven systems.
- Bachelor's or Master's degree in Computer Science, Engineering, or related field; Masters preferred.
- 6 years of professional experience in AI/ML engineering, with at least 3 years in a staff/principal-level role.
- Agentic AI Systems: Hands-on experience with agent-based AI orchestration using LangGraph, LangChain, LlamaIndex, AutoGen, Semantic Kernel, CrewAI, or similar frameworks.
- Tool Connectivity and APIs: Experience with MCP or equivalent protocols, function/tool APIs (OpenAI, Anthropic), and custom system integrations.
- Cloud AI Platforms: Deep experience with Azure AI Foundry, AWS Bedrock, GCP Vertex AI, Databricks Mosaic AI, or Snowflake Cortex AI.
- For full info follow application link.