Software Engineer - Enterprise Engineering [Bay Area]

Palo Alto, CA | Hybrid

NTT DATA AIVista, Inc., a wholly owned subsidiary of NTT DATA, is based in Silicon Valley. We develop AI products that operationalize AI across enterprises operating in complex regulatory environments. We partner with other NTT companies (NTT DATA Inc., NTT DATA Japan, NTT Docomo) to ensure successful deployment to NTT clients. Our clients benefit from AIVista's deep product AI expertise combined with the industry domain and systems integration experience of NTT DATA.

Role Description

AIVista turns foundation models into specialized, governed agents that run mission-critical, regulated enterprise operations. Behind every agent is platform software: services, APIs, integrations, and data pipelines and much of it is shaped by what our customers need.

This is an entry-level role. We hire for strong fundamentals and teach the rest.

AIVista is a service-as-software platform that embeds domain-specific models and agents into customers' real workflows, with governance, observability and agent orchestration built in.

As a Software Engineer, you'll build platform features with and for our customers. You'll join requirement sessions, help turn what a customer needs into a design, write the code, and see it work in their environment. Senior engineers will mentor you and review your work, and you'll own well-defined parts of the platform from design through production.

The role suits someone who wants to learn how enterprise software is built and deployed, and who enjoys working with the people who use it.

Responsibilities

Build platform features — design, implement, and test the services, APIs, and integrations that make up the AIVista platform.

Work directly with customers — take part in requirement discussions, ask the questions that make requirements precise, demo your work, and help debug integration issues in customer environments.

Turn requirements into software — write short designs, break work into steps, and deliver against the requirement, not just the ticket.

Own quality — write well-tested, readable code, and learn and apply our production practices.

Learn fast — pick up new systems, languages, and customer domains as the work requires.

Representative Projects

Build a connector that lets agents read from a customer's system of record, with the authentication and access controls that customer requires.

Implement a data pipeline feature a customer asked for, and write the tests that prove their data stays correct.

Add the observability a customer needs to trust an agent workflow in production.

Fix a performance or reliability problem found during a customer deployment.

Qualifications

Minimum Qualifications

Bachelor's degree in computer science or a related field, or equivalent practical experience.

Hands-on software experience through internships, research, work, or substantial personal projects.

Demonstrated computer science fundamentals in 2 or 3 of the following:

data structures and algorithms, including complexity analysis

operating systems (processes, memory, concurrency)

networking (HTTP, TCP, DNS)

databases (SQL, indexing, transactions)

Proficiency in Python or another modern language (Go, TypeScript, Java, Rust, or similar). You should be able to write code you'd put your name on.

Sound testing and debugging habits. You can find why something is broken, not just that it is.

Clear written and verbal communication, including explaining technical work to non-engineers.

Interest in working directly with customers. (some travel required)

Preferred Qualifications

Experience deploying and running software on a cloud platform (AWS, Azure, or GCP), including containers such as Docker.

Projects using LLM APIs, agent frameworks, or retrieval-augmented generation (RAG).

Internship or project work with real users or external stakeholders.

Independent work — open-source contributions, shipped side projects, or technical writing.

Our interviews focus on fundamentals and how you reason through problems, not framework trivia.