Staff AI Engineer

Vasion

2h•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

Agentic AI Systems: Hands-on experience with agent-based AI orchestration using LangGraph, LangChain, LlamaIndex, AutoGen, Semantic Kernel, CrewAI, or similar frameworks.
Tool Connectivity & 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.
Knowledge & Retrieval: Familiarity with vector databases (Pinecone, Weaviate, Milvus, FAISS), knowledge graphs, and hybrid retrieval systems.
Model Development: Expertise in fine-tuning, embeddings, RAG pipelines, and evaluation frameworks for production models.
MLOps & Observability: Familiarity with LangSmith, MLflow, Weights & Biases, Arize, or Evidently AI for monitoring, evaluation, and governance.
Infrastructure: Strong background in cloud-native architecture, containerization (Docker, Kubernetes), and workflow orchestration (Airflow, Prefect, Dagster, Temporal).
Machine Learning Knowledge (Plus): Understanding of ML techniques (supervised/unsupervised learning, deep learning, reinforcement learning). Experience with ML frameworks such as PyTorch or TensorFlow is a plus but not required.
Demonstrated ability to set technical vision, lead cross-functional initiatives, and mentor engineering teams.
Excellent written and verbal communication skills with the ability to convey complex technical concepts to both technical and non-technical audiences.
Strong problem-solving skills and a proven track record of delivering scalable, enterprise-grade AI solutions.

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 & 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 & 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 & 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 & Governance: Establish monitoring and evaluation frameworks using LangSmith, Weights & Biases, MLflow, Arize, or Evidently AI to ensure reliability, transparency, and compliance.
Innovation & Research: Explore emerging trends in agentic AI, orchestration, and generative systems, applying them to improve intelligence, reliability, and efficiency.
Mentorship & 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.

Benefits

Flexible work environment
Vacation Bonus
Flexible paid time off
Paid parental leave
Competitive pay
A full suite of traditional benefits
Training/Advancement opportunities
401k with company match and immediate vesting
Financial wellness education
Company-contributed HSA
Onsite perks include gym, pickleball, snacks & drinks, arcade, theater room, etc.