STEM OPT AI Data Engineer Jobs
AI Data Engineer roles in machine learning infrastructure, data pipelines, and model deployment qualify for STEM OPT's 24-month extension if your degree is in computer science, data science, or a related STEM field. Your employer must be enrolled in E-Verify, and you'll need a signed I-983 training plan before your start date.
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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 1,568+ AI Data Engineer jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new AI Data Engineer roles.
Get Access To All JobsTips for Finding STEM OPT Authorization as an AI Data Engineer
Verify your CIP code before applying
Check that your degree's Classification of Instructional Programs code maps to an approved STEM field on USCIS's list. Computer science, data science, and information systems CIP codes all qualify, but applied mathematics or statistics degrees sometimes require closer review by your DSO.
Confirm E-Verify enrollment before accepting offers
Search the E-Verify employer search tool by company name before signing anything. Startups and smaller data teams frequently lack E-Verify enrollment, which disqualifies them as STEM OPT employers regardless of how well the role fits your training plan.
Build your I-983 training plan around AI pipeline work
Map your job duties to specific learning objectives in the I-983: model training workflows, data orchestration tools, and MLOps practices all count as structured training activities. A vague plan citing only 'data engineering tasks' increases the risk of DSO rejection or an ICE audit.
Target employers with LCA filing history in AI roles
Use Migrate Mate to filter AI Data Engineer roles by employers who have filed Labor Condition Applications for H-1B workers in similar SOC codes. LCA history signals an existing immigration compliance infrastructure, which matters for STEM OPT employers managing reporting obligations.
Negotiate your offer letter to reflect the I-983 structure
Ask HR to specify your reporting structure, supervision arrangement, and core technical responsibilities in writing before onboarding. ICE's STEM OPT regulations require that the employer mentor you in a formal capacity, and a vague offer letter makes the required I-983 supervisor attestation harder to complete accurately.
Time your OPT application to protect cap-gap eligibility
If you plan to transition to H-1B, file your USCIS I-765 renewal at least 90 days before your initial OPT EAD expires. A timely H-1B registration combined with an in-time OPT filing preserves cap-gap authorization through October 1 if you're selected.
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Find AI Data Engineer JobsFrequently Asked Questions
Does an AI Data Engineer role qualify for the STEM OPT extension?
Yes, if your degree is in an approved STEM field such as computer science, data science, electrical engineering, or applied mathematics, and the role involves substantive technical work in areas like data pipeline development, model deployment, or ML infrastructure. Your DSO confirms eligibility by matching your degree's CIP code to the STEM Designated Degree Program List maintained by USCIS before authorizing the extension.
How do I check whether an employer is enrolled in E-Verify for STEM OPT?
Use the E-Verify employer search tool, which is publicly accessible and searchable by company name and location. Enrollment status can change, so search close to your offer stage rather than at initial application. If a company recruits STEM OPT students but doesn't appear in E-Verify, they must enroll before your extension can be authorized, which adds time to the onboarding process.
What should an AI Data Engineer include in the I-983 training plan?
Your I-983 must describe specific learning goals tied to your job duties, the supervision and mentorship structure, and how the work connects to your STEM degree. For AI Data Engineer roles, this typically includes training objectives around data architecture, feature engineering, model monitoring, and workflow automation tools. Generic descriptions without measurable goals are the most common reason DSOs request revisions before signing.
How do I find AI Data Engineer roles at employers already set up for STEM OPT?
Migrate Mate filters AI Data Engineer listings by employers with verified E-Verify enrollment and LCA filing history in relevant SOC codes, so you're not wasting applications on companies that can't legally hire STEM OPT students. Filtering by this criteria early shortens the offer-to-authorization timeline significantly compared to sorting it out after receiving an offer.
Does the STEM OPT cap-gap rule cover me if I'm working as an AI Data Engineer?
Yes, if your employer files an H-1B petition on your behalf before your OPT EAD expires and you filed your I-765 on time, cap-gap authorization extends your work authorization through September 30 of the fiscal year in which you were selected. This applies regardless of your specific role title, as long as your underlying STEM OPT status is valid and the petition was filed while your OPT period was still active.
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