OPT AI Data Engineer Jobs
AI Data Engineer jobs are among the most OPT-friendly roles in tech. Most require a degree in computer science, data engineering, or a related field, and the work directly matches STEM OPT extension criteria, giving you up to three years of authorized work experience to build toward long-term sponsorship.
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Get Access To All JobsWe are seeking a forward-thinking AI Data Engineer to bridge the gap between our user data assets and advanced AI capabilities. In this role, you will be the architect of our user data foundation, building a robust data warehouse and a dynamic tagging system. Crucially, you will leverage this data to integrate with third-party Large Language Models (LLMs), enabling intelligent, data-driven interactions and next-generation user experiences.
Key Responsibilities
- User Data Warehouse Construction & Architecture
- Design, build, and maintain a scalable User Data Warehouse to consolidate data from fragmented sources.
- Design efficient data models to support high-performance querying and analytics.
-
Implement ETL/ELT pipelines to ensure real-time or near-real-time data availability and quality.
-
Data Tagging & Profile System (User 360)
- Establish a comprehensive User Tagging/Labeling System (User Portrait).
- Develop algorithms to generate static, behavioral, and predictive tags to accurately segment users.
-
Ensure the tagging system is dynamic and can update in real-time to reflect the latest user interactions.
-
LLM Integration & Data Intelligence
- Lead the integration of Large Language Models with our internal data.
-
Design and implement RAG (Retrieval-Augmented Generation) pipelines to feed structured user profile data and tags into LLMs for personalized outputs.
-
Intelligent Interaction Development
- Develop APIs and middleware that allow downstream applications to interact with data using natural language.
- Optimize the "Data-to-AI" loop: ensure the LLM understands the context of the user data to provide accurate, hallucination-free responses.
- Monitor token usage, latency, and response quality of the AI interactions.
Requirements
- Education: Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
- Experience: 3-5+ years of experience in Data Engineering or Backend Development with a focus on data.
- Data Stack:
- Proficiency in SQL and Python/Java/Scala.
- Hands-on experience with Data Warehouses (e.g. Snowflake, BigQuery, ClickHouse) and Big Data frameworks (Spark, Flink).
- Familiar with message middleware (Kafka) and containerization (Docker).
-
User Data Experience: Proven experience in building CDP (Customer Data Platform), DMP, or User Profile/Tagging systems.
-
AI/LLM Skills:
- Experience interacting with LLM APIs (OpenAI, etc.) and inference optimization (vLLM).
- Familiarity with frameworks like LangChain, LlamaIndex, or Haystack.
-
Understanding of Embedding, vector databases (FAISS, Milvus), and RAG architecture.
-
Soft Skills: Strong problem-solving abilities and the ability to translate business needs into technical data requirements.
Preferred Skills (Nice to Haves)
- Experience with Prompt Engineering and optimizing context windows for efficient data feeding.
- Knowledge of Knowledge Graphs (Neo4j, NebulaGraph) and how to combine them with LLMs.
- Experience in model fine-tuning (SFT, RLHF).
- Familiarity with privacy regulations (GDPR/CCPA) regarding user data and AI.
- Experience with mature launched projects serving a large user base on cloud platforms (AWS, etc.).
Benefits
OPPO is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
The US base salary range for this full-time position is $100,000-$300,000 + bonus + long term incentives benefits. Our salary ranges are determined by role, level, and location.
We are seeking a forward-thinking AI Data Engineer to bridge the gap between our user data assets and advanced AI capabilities. In this role, you will be the architect of our user data foundation, building a robust data warehouse and a dynamic tagging system. Crucially, you will leverage this data to integrate with third-party Large Language Models (LLMs), enabling intelligent, data-driven interactions and next-generation user experiences.
Key Responsibilities
- User Data Warehouse Construction & Architecture
- Design, build, and maintain a scalable User Data Warehouse to consolidate data from fragmented sources.
- Design efficient data models to support high-performance querying and analytics.
-
Implement ETL/ELT pipelines to ensure real-time or near-real-time data availability and quality.
-
Data Tagging & Profile System (User 360)
- Establish a comprehensive User Tagging/Labeling System (User Portrait).
- Develop algorithms to generate static, behavioral, and predictive tags to accurately segment users.
-
Ensure the tagging system is dynamic and can update in real-time to reflect the latest user interactions.
-
LLM Integration & Data Intelligence
- Lead the integration of Large Language Models with our internal data.
-
Design and implement RAG (Retrieval-Augmented Generation) pipelines to feed structured user profile data and tags into LLMs for personalized outputs.
-
Intelligent Interaction Development
- Develop APIs and middleware that allow downstream applications to interact with data using natural language.
- Optimize the "Data-to-AI" loop: ensure the LLM understands the context of the user data to provide accurate, hallucination-free responses.
- Monitor token usage, latency, and response quality of the AI interactions.
Requirements
- Education: Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
- Experience: 3-5+ years of experience in Data Engineering or Backend Development with a focus on data.
- Data Stack:
- Proficiency in SQL and Python/Java/Scala.
- Hands-on experience with Data Warehouses (e.g. Snowflake, BigQuery, ClickHouse) and Big Data frameworks (Spark, Flink).
- Familiar with message middleware (Kafka) and containerization (Docker).
-
User Data Experience: Proven experience in building CDP (Customer Data Platform), DMP, or User Profile/Tagging systems.
-
AI/LLM Skills:
- Experience interacting with LLM APIs (OpenAI, etc.) and inference optimization (vLLM).
- Familiarity with frameworks like LangChain, LlamaIndex, or Haystack.
-
Understanding of Embedding, vector databases (FAISS, Milvus), and RAG architecture.
-
Soft Skills: Strong problem-solving abilities and the ability to translate business needs into technical data requirements.
Preferred Skills (Nice to Haves)
- Experience with Prompt Engineering and optimizing context windows for efficient data feeding.
- Knowledge of Knowledge Graphs (Neo4j, NebulaGraph) and how to combine them with LLMs.
- Experience in model fine-tuning (SFT, RLHF).
- Familiarity with privacy regulations (GDPR/CCPA) regarding user data and AI.
- Experience with mature launched projects serving a large user base on cloud platforms (AWS, etc.).
Benefits
OPPO is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
The US base salary range for this full-time position is $100,000-$300,000 + bonus + long term incentives benefits. Our salary ranges are determined by role, level, and location.
See all 1,865+ OPT AI Data Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT AI Data Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as an AI Data Engineer
Target companies with active H-1B filing history
Employers who have sponsored H-1B visas before are far more likely to sponsor yours. Search OFLC disclosure data to verify whether a company has filed for data engineering roles specifically, not just tech roles in general.
Lead with your STEM OPT timeline in outreach
Hiring managers often assume international candidates need sponsorship immediately. Clarifying upfront that you have up to three years of work authorization under STEM OPT removes the biggest objection before it becomes a reason to pass.
Align your skills with the employer's data stack
Fluency in Spark, Airflow, dbt, or cloud platforms like AWS and GCP signals readiness to contribute fast. Employers weigh sponsorship cost against ramp-up time, so demonstrating immediate impact strengthens your case considerably.
Pursue roles where your degree field matches the job directly
H-1B specialty occupation approval depends on a clear connection between your degree and the role. A data engineering position paired with a computer science or information systems degree is among the strongest combinations for petition approval.
Ask about sponsorship policy at the offer stage, not the application stage
Raising sponsorship too early filters you out before you've demonstrated value. Get through the technical interviews first, then ask whether the company has sponsored international hires in data engineering or similar roles before.
Build a portfolio that shows production-level work, not just coursework
Data engineering portfolios should include pipeline architecture, schema design decisions, and performance tradeoffs, not just completed projects. Employers sponsoring visas want evidence you can own infrastructure, not just build assignments under supervision.
AI Data Engineer OPT: Frequently Asked Questions
Does an AI Data Engineer role qualify for the STEM OPT extension?
Yes, AI Data Engineer roles almost always qualify for the 24-month STEM OPT extension, provided your degree is in an eligible STEM field such as computer science, data science, information systems, or electrical engineering. Your employer must also be enrolled in E-Verify, which most mid-size and large tech companies already are. The extension gives you up to three years of total OPT work authorization.
What types of employers are most likely to sponsor an AI Data Engineer for an H-1B?
Technology companies, financial services firms, and large enterprises with dedicated data platforms teams are the most consistent sponsors for data engineering roles. Companies building AI infrastructure at scale, such as those in cloud services, fintech, and health tech, regularly file H-1B petitions for this role. Migrate Mate filters job listings by sponsorship willingness, so you can focus on employers with a real track record rather than guessing.
Can I work as an AI Data Engineer for a startup on OPT?
Yes, but startups carry more risk for OPT students than established employers. If a startup is not enrolled in E-Verify, you cannot use your STEM OPT extension there. Startups also have a lower rate of H-1B sponsorship due to cost and legal overhead. If you pursue a startup role, confirm E-Verify enrollment before accepting and ask directly about their history sponsoring international employees.
How does the 90-day unemployment limit affect AI Data Engineers on OPT?
During your standard OPT period, you cannot accumulate more than 90 days of unemployment. During your STEM OPT extension, that limit increases to 150 cumulative days. For AI Data Engineers, this matters most during job transitions. If you leave one role without another lined up, the clock starts immediately. Keeping your job search active and targeted from the beginning of your OPT period reduces this risk substantially.
What should I look for in an AI Data Engineer job listing to assess sponsorship likelihood?
Look for explicit mentions of visa sponsorship availability, E-Verify enrollment, and whether the company has filed LCAs or H-1B petitions in data engineering or related titles previously. Job listings that specify 'must be authorized to work without sponsorship' are a hard no. Beyond the listing itself, checking a company's public OFLC disclosure filings gives you objective evidence of their actual sponsorship history before you invest time in the application process.