AI Data Specialist Jobs
AI Data Specialist jobs are open across healthcare, finance, technology, and retail, from entry-level to senior and lead roles, with specializations in data labeling, model evaluation, and synthetic data generation. Find a role that fits from the openings below and apply directly.
Find AI Data Specialist JobsLooking for remote work? View remote AI data specialist jobs →Overview
Showing 5 of 267+ AI Data Specialist 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 Specialist Jobs by Experience Level
See All 267+ AI Data Specialist Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Data Specialist JobsAI Data Specialist Job Market
Who's Hiring
- Amazon Web Services29
- Booz Allen Hamilton12
- JPMorganChase10
- Citi4
- Leidos4
Top Industries Hiring
- Technology & Software21
- Education13
- Banking & Financial Services10
- Investment & Asset Management9
- Distribution & Wholesale6
What Employers Look For
The qualifications that appear most often in AI data specialist jobs.
- Bachelor's degree in data science, computer science, linguistics, or a related field
- Proficiency with data annotation platforms such as Scale AI, Labelbox, or CVAT
- Experience with Python for data preprocessing, cleaning, and pipeline scripting
- Familiarity with machine learning concepts including model training, evaluation, and feedback loops
- Strong attention to detail and demonstrated ability to apply complex labeling guidelines consistently
- Experience with SQL or NoSQL databases for querying and managing large datasets
Tips for Your AI Data Specialist Job Search
Quantify your labeling and annotation output
Hiring managers want to see throughput and accuracy together. List the volume of records you've labeled, the tools you used, and your inter-annotator agreement scores or quality metrics so reviewers can benchmark your output against their team's needs.
Apply early to roles that fit
Migrate Mate lists ai data specialist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Tailor your resume to the model lifecycle stage
Job descriptions differ sharply between pre-training data roles and RLHF or fine-tuning roles. Read the posting closely and mirror its language around the pipeline stage, whether that's raw data curation, prompt engineering, or feedback collection.
Build a portfolio with documented edge cases
A short writeup showing how you handled ambiguous or adversarial examples in a labeling task is more compelling than a list of tools. Attach it as a PDF or link a GitHub repo with annotated samples and your decision rationale.
Prepare to demonstrate judgment in take-home tasks
Many ai data specialist interviews include a live or asynchronous annotation exercise. Practice explaining your reasoning aloud as you label, because evaluators care as much about your thinking process as the label you assign.
Negotiate scope before accepting a contract rate
Contract ai data specialist roles often have vague task definitions that expand after you start. Before accepting, ask for a sample task batch, the expected daily volume, and whether the rate is per item or per hour to avoid scope creep.
AI Data Specialist Jobs: Frequently Asked Questions
Which companies are hiring the most ai data specialists?
The companies hiring the most ai data specialists right now include Amazon Web Services, Booz Allen Hamilton, and JPMorganChase, with the largest share of openings in Virginia, New York, and California, based on current listings on Migrate Mate as of August 2026. Demand is particularly concentrated at companies running large-scale model training and evaluation programs.
How many ai data specialist jobs are remote?
About 46% of ai data specialist openings are fully remote or hybrid as of August 2026, making it one of the more location-flexible roles in the data field. Remote work is most common in text annotation, prompt evaluation, and RLHF feedback roles, while on-site positions tend to appear in labs handling sensitive or proprietary datasets.
How do you become an ai data specialist?
Start by building hands-on experience with a publicly available annotation tool and completing a self-directed labeling project you can show to employers. Learn Python basics for data wrangling, then study how machine learning pipelines consume labeled data. Earn a credential in data annotation or AI quality assurance, and apply to entry-level or contract roles to build a documented work history with real throughput and accuracy metrics.
Can you get hired as an ai data specialist with little experience?
Yes, many ai data specialist roles are structured to accept candidates without prior professional experience if you can demonstrate accuracy and consistency on a labeling task. Build a small portfolio by completing free or paid micro-task annotation projects, document your quality scores, and apply to contract or part-time roles first. Those short engagements provide the throughput history that full-time hiring managers look for.
What does the ai data specialist interview process look like?
Most ai data specialist interviews begin with a recruiter screen focused on your familiarity with annotation workflows and data quality principles. A take-home or live annotation exercise follows, where you label a sample set and explain your decisions. A final round typically involves a technical conversation with a data or ML team member covering your approach to edge cases, disagreement resolution, and guideline interpretation.
Where can I find and apply to ai data specialist jobs?
You can find and apply to ai data specialist jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the listings for roles that match your experience level and specialization, then apply directly to each position that fits.
See All 267+ AI Data Specialist Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Data Specialist Jobs