AI Data Engineer Jobs
AI Data Engineer jobs are open across technology, healthcare, financial services, and retail, from entry-level to staff and principal, with specializations in machine learning pipelines, feature stores, and real-time data infrastructure. Find a role that fits from the openings below and apply directly.
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Showing 5 of 1,127+ AI Data Engineer jobs
Job Responsibilities:
- Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
- Write clean and efficient code, and ensure code maintainability and reusability.
- Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
- Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
- Perform code reviews to ensure code quality and consistency.
- Conduct testing to ensure application quality and reliability.
- Create and maintain technical documentation for web applications.
- Participate in project planning, estimation, and prioritization.
- Stay up to date with the latest technologies for Python development.
- Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
- Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
Requirement:
- Experience in software development in Python3.
- Decent understanding of the software development/testing life cycle.
- Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
- Experience with version control tools, such as Git.
- Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
- Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
- Solid knowledge of vector search concepts and at least one vector DB in production.
- Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.
Preferred Qualifications:
- Master’s Degree in Computer Science, Software Engineering, or related field.
- 1+ year professional experience in Python web application development with either Flask or Django.
- Experience in RESTful API development in Python.
- Understanding of Python web application frameworks such as Flask or Django.
- Experience with Cloud services, such as AWS.
- Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
- Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
- Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
- Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
- Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).
BeaconFire is an E-verified company and provides equal employment opportunities (visa sponsorship provided).
Job Responsibilities:
- Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
- Write clean and efficient code, and ensure code maintainability and reusability.
- Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
- Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
- Perform code reviews to ensure code quality and consistency.
- Conduct testing to ensure application quality and reliability.
- Create and maintain technical documentation for web applications.
- Participate in project planning, estimation, and prioritization.
- Stay up to date with the latest technologies for Python development.
- Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
- Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
Requirement:
- Experience in software development in Python3.
- Decent understanding of the software development/testing life cycle.
- Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc).
- Experience with version control tools, such as Git.
- Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
- Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
- Solid knowledge of vector search concepts and at least one vector DB in production.
- Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.
Preferred Qualifications:
- Master’s Degree in Computer Science, Software Engineering, or related field.
- 1+ year professional experience in Python web application development with either Flask or Django.
- Experience in RESTful API development in Python.
- Understanding of Python web application frameworks such as Flask or Django.
- Experience with Cloud services, such as AWS.
- Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
- Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
- Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
- Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
- Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).
BeaconFire is an E-verified company and provides equal employment opportunities (visa sponsorship provided).
AI Data Engineer Jobs by Experience Level
Top Cities Hiring AI Data Engineers
Explore AI data engineer openings in the cities hiring most right now.
See All 1,127+ AI Data Engineer Jobs
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Find AI Data Engineer JobsAI Data Engineer Job Market
Who's Hiring
- Apple42
- Amazon Web Services36
- JPMorganChase29
- Deloitte26
- Amazon21
Top Industries Hiring
- Technology & Software107
- Consulting & Professional Services44
- Investment & Asset Management28
- Electronics & Hardware25
- Banking & Financial Services21
What Employers Look For
The qualifications that appear most often in AI data engineer jobs.
- Proficiency in Python and SQL for data pipeline development and transformation
- Experience building and maintaining ML feature pipelines or data platforms at scale
- Hands-on work with orchestration tools such as Apache Airflow, Prefect, or Dagster
- Familiarity with cloud data platforms including AWS, GCP, or Azure data services
- Knowledge of streaming frameworks such as Apache Kafka or Apache Flink
- Bachelor's degree in computer science, data engineering, or a related technical field
Tips for Your AI Data Engineer Job Search
Tailor your resume to pipeline depth
Hiring managers for ai data engineer roles want to see end-to-end ownership, not just tool lists. Show exactly which stages of a data pipeline you designed, the scale it ran at, and the business outcome it supported.
Highlight ML pipeline tooling explicitly
Generic 'data engineering' resumes get filtered out before a human reads them. Call out specific orchestration tools like Airflow or Prefect, feature stores like Feast, and model-serving infrastructure so your resume clears automated screening.
Target roles by data stack, not just title
AI data engineer job descriptions vary widely. Filter openings by the specific stack you know best, whether that's Spark and Databricks, dbt and Snowflake, or Kafka and Flink, so you apply where your experience maps cleanest.
Apply early to roles that fit
Migrate Mate lists ai data engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prep for system design at the data layer
Most ai data engineer interviews include a system design round focused on designing scalable feature pipelines or real-time ingestion systems. Practice scoping data freshness requirements, partitioning strategies, and failure-recovery patterns before your interview.
Negotiate with infrastructure cost data
When you reach the offer stage, frame your value around measurable infrastructure outcomes you have delivered, such as reduced query latency or lower cloud compute costs. Concrete cost or reliability numbers strengthen your position more than general experience claims.
AI Data Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai data engineers?
The companies hiring the most ai data engineers right now include Apple, Amazon Web Services, and JPMorganChase, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in technology, financial services, and healthcare organizations investing in production machine learning systems.
How many ai data engineer jobs are remote?
About 62% of ai data engineer openings are fully remote or hybrid as of August 2026, reflecting strong remote adoption across data infrastructure roles. Sub-areas focused on cloud-native pipeline development and ML platform engineering tend to have the highest share of fully remote positions, since the work is tool-driven and asynchronous by nature.
How do you become an ai data engineer?
Start by building strong fundamentals in Python, SQL, and distributed data systems, then move into hands-on work with orchestration tools and cloud data platforms. Contributing to open-source data projects or building a portfolio of end-to-end ML pipelines demonstrates practical ability. Many practitioners transition from data engineering or software engineering roles by taking on ML infrastructure work within their current team before moving into a dedicated ai data engineer position.
Can you get an ai data engineer job with little experience?
Entry-level ai data engineer roles exist, but they usually require demonstrated pipeline-building ability even without years of professional experience. Building and publishing at least one project that ingests real data, runs transformations, and feeds a model endpoint does more for your candidacy than certifications alone. Applying to companies actively expanding their ML platform teams increases your chances, since those teams often hire candidates who show strong fundamentals and can grow into the role.
What does the ai data engineer interview process look like?
Most ai data engineer interviews include a recruiter screen, a technical phone interview covering Python and SQL, a system design round focused on designing data or feature pipelines at scale, and a final loop with engineering and data science stakeholders. Some companies add a take-home component asking you to build or debug a pipeline. Behavioral questions typically probe cross-functional collaboration with data scientists and ML engineers.
Where can I find and apply to ai data engineer jobs?
You can find and apply to ai data engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and specialization, then apply directly to each listing from the page.
See All 1,127+ AI Data Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Data Engineer Jobs