AI Deployment Engineer, Codex | Singapore

ID: 9291

Type: Full-time

Category: Others

Company Name: Rockset

Location: Singapore - Singapore - Singapore

Education Level: Senior (5-10 years)

Visit company vacancy

Job Description

About the team

The Codex Deployment Engineering team helps customers adopt OpenAI's coding tools throughout their software development lifecycle. We act as trusted technical partners, guiding engineering teams as they integrate Codex into their projects and workflows. Our customers span digital-native companies to global enterprises, and we work side-by-side to accelerate how they plan, build, and deliver software.

About the Role

We are seeking a technically deep, creativity-driven AI Deployment Engineer who is already a power user of AI coding tools and passionate about pushing the boundaries of developer productivity. You will partner directly with engineering leaders and hands-on builders to design, validate, and scale advanced AI workflows, often using Codex to prototype and build the very demos, integrations, and automations customers ultimately adopt.

This is a highly cross-functional role that blends technical architecture, product strategy, and customer-facing leadership. You’ll work closely with Sales, Solutions Engineering, Product, Applied Engineering, and the broader Codex organization to advocate for customer needs, shape product direction, and accelerate the successful deployment of intelligent coding systems across some of the world’s most influential companies.

This role is based in Singapore.

In this role, you will:

Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows.
Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout.
Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of your development process.
Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely.
Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex.
Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams.
Influence customer strategy and decision-making by framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows.
Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

You’ll thrive in this role if you:

Have 8+ years of post-sales engineering or solutions architecture, working directly with customers.
Have 2+ years of software engineering experience and a strong understanding of the software development life cycle.
Are an active power user of AI coding tools and have deeply customized your own developer workflow; you have a point of view on what makes engineers more productive.
Enjoy building scrappy, high-signal demos, integrations, and prototypes that clearly articulate what Codex can enable, often using Codex to accelerate your own development process.
Have experience delivering large, high-impact workshops or technical training to engineering teams and know how to craft sessions that are engaging, hands-on, and outcomes-driven.
Have contributed technical guides, patterns, or examples publicly and care about clarity, pedagogy, and community impact.
Communicate complex technical concepts in clear, persuasive written and verbal form especially when helping customers make strategic decisions about where and how to apply AI.
Are excited by ambiguous, rapidly evolving problem spaces and enjoy iterating toward novel solutions hand-in-hand with customers.
Care about customer success, reliability, safety, and operational excellence as much as you care about technical ingenuity.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.

OpenAI Global Applicant Privacy Policy

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Company Information

Company Name: Rockset

Company Website: https://rockset.com

Company Address: San Mateo, California, USA

Rockset is a cloud-native data platform company that builds and operates a managed, real-time analytics database designed to enable fast, low-latency SQL queries on continuously changing data streams and operational data stores. The company positions its core product as a purpose-built service for application developers, analytics engineers, and data teams who need to run complex queries—including joins, aggregations, and full-text search—against high-volume, high-velocity data without managing database infrastructure. Rockset’s offering emphasizes instant ingestion, automatic indexing, and serverless scalability so customers can get real-time insights from event streams, operational databases, and data lakes with minimal operational overhead. At the center of Rockset’s business activities is a managed database service delivered from the cloud. The service is designed to ingest data from a variety of sources—change data capture (CDC) streams, message queues, object storage, and direct connectors to operational databases—and make that data queryable immediately. Rockset automates the work that traditional analytics stacks require: it handles continuous ingestion, schema evolution, indexing, resource provisioning, and query optimization. The company provides a hosted, fully managed experience that abstracts the underlying infrastructure and offers predictable developer ergonomics and APIs. Rockset’s main product is the Rockset cloud database service, a serverless, managed analytics engine that supports ANSI SQL and enables low-latency queries on fresh data. Key product capabilities include continuous data ingestion from sources such as streaming platforms and change streams, automatic and adaptive indexing (often referred to as a “converged index” approach combining row, columnar, and inverted-index elements), a SQL query interface for expressive analytics and search, and a pay-for-what-you-use consumption model. Because of the indexing approach and the system’s ability to shard and parallelize queries across cloud infrastructure, Rockset aims to deliver millisecond-to-subsecond query latencies even on complex queries over large, rapidly changing datasets. Integration and extensibility are important parts of Rockset’s product strategy. The platform provides connectors and ingestion paths for common cloud and data stack components, enabling users to stream or batch data from sources such as message brokers, operational databases (via CDC), cloud object storage, and data warehouses. On the query and consumption side, Rockset supports standard interfaces that allow integration with applications and analytics tools: developers can query with SQL through REST APIs, client libraries, and standard connectors so that Rockset can be used as a back-end for dashboards, embedded analytics, personalization, recommendation engines, and search-oriented application features. Rockset also provides capabilities to join and enrich streaming or operational data with historical datasets, enabling hybrid analytical workloads that combine fresh and reference data. Operational characteristics of Rockset’s managed service emphasize elasticity and minimal operational burden. The platform is designed to be serverless from the user’s perspective—resources scale automatically to meet query and ingestion demand, and the user pays for compute and storage consumption rather than provisioning fixed clusters. Rockset also focuses on handling schema changes automatically as incoming data evolves, reducing the need for manual schema migrations that are common in traditional data warehouses. The managed nature of the service includes monitoring, automated failure recovery, and tuning handled by the platform, allowing engineering teams to focus on application logic and analytics rather than database administration. From a usage perspective, Rockset targets a set of real-time and near-real-time analytics use cases where freshness, performance, and developer productivity are critical. Typical applications include real-time dashboards and observability, customer 360 and personalization use cases, operational analytics, search-and-query experiences embedded in user-facing applications, and ad-hoc analytics on streaming event data. Rockset’s SQL-first approach helps lower the barrier to entry for analytics and data engineering teams already familiar with relational query language while also serving developers who need to embed queries in production applications. Security, governance, and enterprise readiness are presented as part of the managed offering: Rockset provides typical enterprise controls such as authentication and authorization, support for private network connectivity to cloud resources, and features intended to meet organizational security policies. The platform also exposes monitoring and auditing capabilities that help teams observe query performance and data flows. Rockset’s positioning is that customers can get the agility of real-time analytics while retaining operational safeguards required by corporate and regulatory environments. The company’s go-to-market focus combines direct product-led adoption—where developers can sign up and begin ingesting data and running queries quickly—with enterprise engagement for larger or production deployments that require integration, security reviews, and architectural guidance. Rockset’s documentation, SDKs, and examples emphasize rapid onboarding and practical recipes for building analytics and search features on top of streaming and operational data. Overall, Rockset is widely characterized in public materials as a cloud-native, real-time analytics database that abstracts infrastructure complexity and provides a SQL interface for immediate querying of continuously updated data. The product is aimed at teams that need real-time insights and application-facing analytics without the cost and complexity of managing specialized infrastructure or building custom indexing and ingestion pipelines.

Visit company vacancy