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.
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Showing 5 of 53+ AI Data Specialist jobs
GENERAL INFORMATION
Req #
WD00099074
Career area:
Artificial Intelligence
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Wednesday, May 27, 2026
Working time:
Full-time
ADDITIONAL LOCATIONS:
* United States of America - North Carolina - Morrisville
WHY WORK AT LENOVO
We are Lenovo. We do what we say. We own what we do. We WOW our customers.
Lenovo is a US$83 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Guided by its vision of “Smarter Technology for All”, Lenovo is executing a Hybrid AI strategy that spans Personal AI – one personal AI, multiple devices; and Enterprise AI – helping customers turn data into insights and value. This strategy is delivered through the Group’s commitment to world-class innovation and a full-stack AI portfolio, including devices (PCs, workstations, smartphones, tablets, accessories), infrastructure solutions (server, storage, edge, high performance computing and software defined infrastructure), as well as software, solutions, and services. With a global footprint spanning 21 research and development locations in 11 markets, and a global supply chain including more than 30 manufacturing sites across 10 markets, Lenovo is widely recognized for its operational excellence, including ranking #8 in the Gartner Supply Chain Top 25. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).
DESCRIPTION AND REQUIREMENTS
As an AI Data Specialist, you will play a key role in the design, development, and deployment of an Agentic AI platform that streamlines the creation of AI-driven customer solutions. You will work closely with AI engineers and cross-functional teams to support the deployment, integration, and scaling of AI services across enterprise environments. In this role, you will leverage your expertise in data management, AI technologies, and development practices to build efficient, reusable, and scalable data-driven solutions. You will be responsible for ensuring that data pipelines, models, and AI components are effectively integrated into a cohesive and high-performing platform. To succeed, you should possess a strong understanding of AI and data ecosystems, demonstrate hands-on technical skills, and have the ability to transform complex data and AI tools into standardized, repeatable solutions that drive business value.
This role sits within Lenovo’s Solutions & Services Group (SSG), the global organization that brings together our end-to-end AI solutions and services to turn customer vision into value. You’ll be joining a new, distributed engineering team building the xIQ Agent Platform, an AI-native delivery platform that powers Lenovo’s Agentic AI strategy across hybrid cloud, on-prem, and edge.
- Design, develop, and implement data-driven AI solutions for an Agentic AI platform, aligning with enterprise architecture and business objectives.
- Build and maintain scalable data processing pipelines for AI workloads, including data ingestion, cleansing, transformation, and feature engineering.
- Develop and deploy end-to-end AI systems using LLMs, SLMs, and VLMs for advanced data processing, enrichment, and automation.
- Leverage NVIDIA AI technologies (e.g., CUDA, NV-Ingest, VLM..etc) or similar platforms to optimize model training, fine-tuning, and inference performance.
- Implement and manage vector databases such as Milvus, PostgreSQL (PGVector), or other vector stores to support semantic search, embeddings, and Retrieval-Augmented Generation (RAG) use cases.
- Design and optimize data and retrieval pipelines that integrate structured and unstructured data with LLM-based reasoning systems.
- Develop and integrate AI components (APIs, microservices, inference layers) into enterprise platforms across cloud, on-premise, and edge environments.
- Select and implement data engineering and pipeline orchestration tools to ensure scalable and reliable data workflows.
- Apply best practices in data engineering, MLOps, and AIOps, including pipeline monitoring, versioning, and performance tuning.
- Ensure data security, governance, and compliance, including encryption, access control, and secure data handling practices.
- Write clean, maintainable, and reusable code following enterprise development standards and best practices.
- Create detailed technical documentation, including data flow architectures, pipeline designs, model integration patterns, and system interfaces.
- Continuously evaluate and adopt emerging AI, LLM, and data technologies to improve solution effectiveness and scalability.
- Support solution design discussions, PoCs, and pre-sales activities by providing expertise in AI data architecture and implementation.
- Effectively communicate technical designs, data strategies, and AI solutions to both technical teams and business stakeholders.
BASIC QUALIFICATIONS:
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
- 3-5+ years of experience in AI/data engineering and implementation roles, including data engineering, machine learning, NLP, or Generative AI-focused data solutions.
PREFERRED QUALIFICATIONS:
- 2+ years of hands-on experience in building end-to-end Generative AI and data-driven solutions, including data preprocessing, embedding generation, and Retrieval-Augmented Generation (RAG) pipelines using LLMs and multimodal models (e.g., OpenAI, Anthropic, Llama, Hugging Face, Amazon Bedrock).
- Strong experience with data processing frameworks and pipeline development.
- Hands-on experience with vector databases such as Milvus, PostgreSQL (PGVector), Pinecone, or similar, including embedding management and semantic search implementations.
- Experience working with the NVIDIA AI ecosystem (e.g., GPUs, CUDA, TensorRT, NeMo) or equivalent acceleration technologies for efficient data processing, model training, and inference is a strong plus.
- Strong understanding of data architectures involving structured, semi-structured, and unstructured data, and building scalable pipelines to support AI/ML workloads.
- Experience integrating AI/data solutions across cloud and on-premise environments, including containerization and orchestration using Docker and Kubernetes.
- Proven experience implementing MLOps, LLMOps, and DataOps practices, including CI/CD pipelines, data versioning, monitoring, and lifecycle management using tools such as Jenkins, GitLab, MLflow, or similar.
- Strong programming skills in Python, with experience in data processing libraries (e.g., Pandas, PySpark), and familiarity with API development.
- Solid understanding of data governance, security, and compliance, including handling sensitive data and implementing access controls and encryption.
- Strong problem-solving skills and ability to translate complex data and AI requirements into scalable, reusable solutions.
This role offers the flexibility to be home-based anywhere in the U.S. with preference for the Eastern time zone. If you're near our Raleigh office, we follow a friendly hybrid model with three days a week in the office—great for collaboration and connection!
The base salary range budgeted for this position is $140,000 to $170,000. Individuals may also be considered for bonuses and/or commissions. Lenovo’s various benefits can be found at www.lenovobenefits.com
In compliance with Colorado's EPEWA, the expected Application Deadline for this position is 7/1/2026 - this applies to both internal and external candidates.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.
ADDITIONAL LOCATIONS:
United States of America - North Carolina - Morrisville
United States of America
United States of America - North Carolina
United States of America - North Carolina - Morrisville
GENERAL INFORMATION
Req #
WD00099074
Career area:
Artificial Intelligence
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Wednesday, May 27, 2026
Working time:
Full-time
ADDITIONAL LOCATIONS:
* United States of America - North Carolina - Morrisville
WHY WORK AT LENOVO
We are Lenovo. We do what we say. We own what we do. We WOW our customers.
Lenovo is a US$83 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Guided by its vision of “Smarter Technology for All”, Lenovo is executing a Hybrid AI strategy that spans Personal AI – one personal AI, multiple devices; and Enterprise AI – helping customers turn data into insights and value. This strategy is delivered through the Group’s commitment to world-class innovation and a full-stack AI portfolio, including devices (PCs, workstations, smartphones, tablets, accessories), infrastructure solutions (server, storage, edge, high performance computing and software defined infrastructure), as well as software, solutions, and services. With a global footprint spanning 21 research and development locations in 11 markets, and a global supply chain including more than 30 manufacturing sites across 10 markets, Lenovo is widely recognized for its operational excellence, including ranking #8 in the Gartner Supply Chain Top 25. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).
DESCRIPTION AND REQUIREMENTS
As an AI Data Specialist, you will play a key role in the design, development, and deployment of an Agentic AI platform that streamlines the creation of AI-driven customer solutions. You will work closely with AI engineers and cross-functional teams to support the deployment, integration, and scaling of AI services across enterprise environments. In this role, you will leverage your expertise in data management, AI technologies, and development practices to build efficient, reusable, and scalable data-driven solutions. You will be responsible for ensuring that data pipelines, models, and AI components are effectively integrated into a cohesive and high-performing platform. To succeed, you should possess a strong understanding of AI and data ecosystems, demonstrate hands-on technical skills, and have the ability to transform complex data and AI tools into standardized, repeatable solutions that drive business value.
This role sits within Lenovo’s Solutions & Services Group (SSG), the global organization that brings together our end-to-end AI solutions and services to turn customer vision into value. You’ll be joining a new, distributed engineering team building the xIQ Agent Platform, an AI-native delivery platform that powers Lenovo’s Agentic AI strategy across hybrid cloud, on-prem, and edge.
- Design, develop, and implement data-driven AI solutions for an Agentic AI platform, aligning with enterprise architecture and business objectives.
- Build and maintain scalable data processing pipelines for AI workloads, including data ingestion, cleansing, transformation, and feature engineering.
- Develop and deploy end-to-end AI systems using LLMs, SLMs, and VLMs for advanced data processing, enrichment, and automation.
- Leverage NVIDIA AI technologies (e.g., CUDA, NV-Ingest, VLM..etc) or similar platforms to optimize model training, fine-tuning, and inference performance.
- Implement and manage vector databases such as Milvus, PostgreSQL (PGVector), or other vector stores to support semantic search, embeddings, and Retrieval-Augmented Generation (RAG) use cases.
- Design and optimize data and retrieval pipelines that integrate structured and unstructured data with LLM-based reasoning systems.
- Develop and integrate AI components (APIs, microservices, inference layers) into enterprise platforms across cloud, on-premise, and edge environments.
- Select and implement data engineering and pipeline orchestration tools to ensure scalable and reliable data workflows.
- Apply best practices in data engineering, MLOps, and AIOps, including pipeline monitoring, versioning, and performance tuning.
- Ensure data security, governance, and compliance, including encryption, access control, and secure data handling practices.
- Write clean, maintainable, and reusable code following enterprise development standards and best practices.
- Create detailed technical documentation, including data flow architectures, pipeline designs, model integration patterns, and system interfaces.
- Continuously evaluate and adopt emerging AI, LLM, and data technologies to improve solution effectiveness and scalability.
- Support solution design discussions, PoCs, and pre-sales activities by providing expertise in AI data architecture and implementation.
- Effectively communicate technical designs, data strategies, and AI solutions to both technical teams and business stakeholders.
BASIC QUALIFICATIONS:
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field.
- 3-5+ years of experience in AI/data engineering and implementation roles, including data engineering, machine learning, NLP, or Generative AI-focused data solutions.
PREFERRED QUALIFICATIONS:
- 2+ years of hands-on experience in building end-to-end Generative AI and data-driven solutions, including data preprocessing, embedding generation, and Retrieval-Augmented Generation (RAG) pipelines using LLMs and multimodal models (e.g., OpenAI, Anthropic, Llama, Hugging Face, Amazon Bedrock).
- Strong experience with data processing frameworks and pipeline development.
- Hands-on experience with vector databases such as Milvus, PostgreSQL (PGVector), Pinecone, or similar, including embedding management and semantic search implementations.
- Experience working with the NVIDIA AI ecosystem (e.g., GPUs, CUDA, TensorRT, NeMo) or equivalent acceleration technologies for efficient data processing, model training, and inference is a strong plus.
- Strong understanding of data architectures involving structured, semi-structured, and unstructured data, and building scalable pipelines to support AI/ML workloads.
- Experience integrating AI/data solutions across cloud and on-premise environments, including containerization and orchestration using Docker and Kubernetes.
- Proven experience implementing MLOps, LLMOps, and DataOps practices, including CI/CD pipelines, data versioning, monitoring, and lifecycle management using tools such as Jenkins, GitLab, MLflow, or similar.
- Strong programming skills in Python, with experience in data processing libraries (e.g., Pandas, PySpark), and familiarity with API development.
- Solid understanding of data governance, security, and compliance, including handling sensitive data and implementing access controls and encryption.
- Strong problem-solving skills and ability to translate complex data and AI requirements into scalable, reusable solutions.
This role offers the flexibility to be home-based anywhere in the U.S. with preference for the Eastern time zone. If you're near our Raleigh office, we follow a friendly hybrid model with three days a week in the office—great for collaboration and connection!
The base salary range budgeted for this position is $140,000 to $170,000. Individuals may also be considered for bonuses and/or commissions. Lenovo’s various benefits can be found at www.lenovobenefits.com
In compliance with Colorado's EPEWA, the expected Application Deadline for this position is 7/1/2026 - this applies to both internal and external candidates.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.
ADDITIONAL LOCATIONS:
United States of America - North Carolina - Morrisville
United States of America
United States of America - North Carolina
United States of America - North Carolina - Morrisville
See All 53+ AI Data Specialist Jobs
Jump back to the full list of openings and apply to any AI data specialist role that fits.
Find AI Data Specialist JobsAI Data Specialist Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- LTIMindtree10
- Anaplan2
- Apple2
- Dell Technologies2
- NiCE2
Top Industries Hiring
- Technology & Software22
- Electronics & Hardware6
- Consulting & Professional Services4
- Energy3
- Investment & Asset Management3
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 LTIMindtree, Anaplan, and Apple, with the largest share of openings in Texas, California, and Georgia, based on current listings on Migrate Mate as of June 2026. Demand is particularly concentrated at companies running large-scale model training and evaluation programs.
How many ai data specialist jobs are remote?
About 30% of ai data specialist openings are fully remote or hybrid as of June 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 53+ AI Data Specialist Jobs
Jump back to the full list of openings and apply to any AI data specialist role that fits.
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