AI Engineer

About the role

The role designs and delivers multi-agent AI systems for enterprise automation. The team builds scalable, secure AI technologies that integrate with platforms like SAP, Salesforce, Workday, Snowflake, and MuleSoft. Success is measured by reliable, high-impact automation that advances enterprise workflows.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Complex agentic pipelines are developed around open- and closed-source LLMs to advance multi-step automation for enterprise workflows.

Purpose-built tools connect AI capabilities with enterprise software platforms such as SAP, Salesforce, Workday, Snowflake, and MuleSoft.

AI designs are guided by business objectives and industry best practices to ensure reliable, measurable outcomes for enterprise users.

Custom ML models are trained and fine-tuned using data generation and processing pipelines to address evolving enterprise requirements.

LLM training and evaluation pipelines are implemented and managed to maintain model quality and performance over time.

AI infrastructure is managed across major cloud platforms including AWS, Azure, and GCP to support production reliability.

Inference endpoints are deployed and served as part of core product infrastructure to deliver AI and LLM technologies at scale.

Requirements

A Bachelor's degree, Master's degree or Ph.D. in Computer Science, Engineering, or a related field is required.

At least 5 years of experience in software engineering, platform engineering, or related roles is required.

Demonstrated expertise in AI model building and agent development is required.

A strong background in ML and LLM-driven systems development is required.

Experience with compound AI systems, vector databases, and toolcalling AIs is required.

Strong problem-solving skills are required to thrive in a fast-paced, collaborative environment.

Practical notes

This US-based role requires authorization to work in the United States. Work is performed from the San Jose Office (HQ). Some travel may be required.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Roles in this field focus on applied AI and production systems. Tooling commonly includes LLM frameworks, vector databases, and cloud infrastructure. Collaboration across product, engineering, and operations is typical. Rapid iteration is common in high-growth environments. Clear ownership and measurable impact are emphasized in production-grade AI systems.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.