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Ml Data Associate Jobs in Raleigh, NC (NOW HIRING)

Principal Data Architect

Raleigh, NC · On-site

$160K - $240K/yr

Architect end-to-end MLOps capabilities, including model lifecycle management, CI/CD for ML ... associates. More information can be found at Qualifications: Bachelor's Degree and 10 years of ...

Azure or Databricks certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, Databricks ML Professional, Databricks Data Engineer Professional) are a plus. We are GEI.

AI Engineer

Cary, NC

$110K - $150K/yr

Azure or Databricks certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, Databricks ML Professional, Databricks Data Engineer Professional) are a plus. We are GEI.

AWS Solutions Architect Associate or higher preferred. • Comfortable working closely with ... Exposure to enterprise AI/ML, analytics, or data platform modernization initiatives. • ...

Showing results 41-60

Ml Data Associate information

See Raleigh, NC salary details

$55.9K

$66.1K

$125.4K

How much do ml data associate jobs pay per year?

As of Aug 22, 2026, the average yearly pay for ml data associate in Raleigh, NC is $66,139.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,400.00 and $57,800.00 per year, depending on experience, location, and employer.

What is an ml data associate?

ML Data Associates are professionals who support machine learning projects by preparing, labeling, and validating data used to train and evaluate algorithms. They often work with large datasets, ensuring data quality and accuracy, and may use specialized tools to annotate images, text, or audio. Their work is essential for enabling machine learning models to learn from high-quality, well-structured data, and they often collaborate with data scientists and engineers to optimize data pipelines.

What are the key skills and qualifications needed to thrive as an ml data associate?

To thrive as an ML Data Associate, you need strong analytical skills, attention to detail, and a solid understanding of data annotation or labeling, often supported by a degree in a technical field. Familiarity with data labeling tools, basic programming (such as Python), and experience working with machine learning platforms are typically required. Excellent communication, problem-solving abilities, and the capacity to work efficiently in teams are important soft skills. These skills ensure high-quality, accurately labeled datasets that are essential for training effective machine learning models.

What are some common challenges faced by ml data associates when labeling complex datasets, and how can they be effectively addressed?

ML Data Associates often encounter challenges with ambiguous data, inconsistent labeling guidelines, or rapidly evolving project requirements. To address these, it's important to maintain open communication with data scientists and project leads, ask clarifying questions, and participate in regular calibration sessions to ensure consistency. Utilizing annotation tools efficiently and staying up-to-date with best practices can also help manage complexity and improve label quality. Collaboration and feedback within the team are key to overcoming these challenges and ensuring high-quality datasets.

What is the difference between Ml Data Associate vs Data Analyst?

AspectML Data AssociateData Analyst
Required CredentialsTypically a degree in computer science, data science, or related field; familiarity with machine learning conceptsUsually a degree in statistics, mathematics, or business analytics; strong Excel and data visualization skills
Work EnvironmentTech companies, AI startups, or organizations focusing on machine learning projectsBusiness, finance, marketing, and consulting firms analyzing data for insights
Employer & Industry UsageUsed in industries developing AI models, machine learning pipelines, and data infrastructureCommon across industries for reporting, trend analysis, and strategic decision-making

While both roles involve working with data, ML Data Associates focus on preparing and managing data specifically for machine learning models, whereas Data Analysts interpret data to generate business insights. The roles overlap in data handling skills but differ in their end goals and technical focus.

How do I become an ML Data Associate?

To become an ML Data Associate, candidates typically need a high school diploma or equivalent, along with strong attention to detail and organizational skills. Familiarity with data management tools, basic understanding of machine learning concepts, and experience with data annotation or labeling are often required. Some roles may also require knowledge of programming languages like Python or experience with data annotation platforms.

What skills do you need for a machine learning data associate job?

A machine learning data associate needs strong analytical skills, attention to detail, and proficiency in data management tools like Excel, SQL, or Python. Knowledge of data cleaning, labeling, and basic understanding of machine learning concepts are also important for the role.

What job categories do people searching Ml Data Associate jobs in Raleigh, NC look for?

The top searched job categories for Ml Data Associate jobs in Raleigh, NC are:

What cities near Raleigh, NC are hiring for Ml Data Associate jobs?

Cities near Raleigh, NC with the most Ml Data Associate job openings:

Infographic showing various Ml Data Associate job openings in Raleigh, NC as of June 2026, with employment types broken down into 74% Full Time, 22% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $66,067 per year, or $31.8 per hour.

Principal AI Architect

First Citizens Bank

Raleigh, NC • On-site

$160K - $240K/yr

Full-time

Re-posted 18 days ago


First Citizens Bank rating

7.4

Company rating: 7.4 out of 10

Based on 106 frontline employees who took The Breakroom Quiz

106th of 171 rated banks


Job description

Overview
The Principal AI Architect is a senior technical leadership role responsible for defining and governing enterprise-wide AI, GenAI, and MLOps architecture. This role sets the long-term vision, reference architectures, and reusable patterns for scalable, secure, and responsible AI adoption across the organization. This role combines strong hands-on engineering with strategic innovation to design, prototype, and deliver intelligent, data-driven solutions that power analytics, machine learning, and next-generation AI applications.
Responsibilities
Enterprise AI & GenAI Architecture
  • Define and own enterprise AI and GenAI reference architectures, including LLM platforms, RAG patterns, agentic systems, and multimodal solutions.
  • Establish standardized architectural patterns for model serving, prompt management, orchestration, tool use, and agent frameworks.
  • Lead architecture decisions for buy vs. build, model selection, hosting strategies, and vendor integrations.
  • Ensure AI architectures align with enterprise standards, cloud strategy, security, and governance.

AI Platform Engineering & MLOps
  • Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems.
    Architect end-to-end MLOps capabilities, including model lifecycle management, CI/CD for ML, feature stores, model monitoring, and drift detection.
  • Define enterprise patterns for training, fine-tuning, deployment, and observability of ML and GenAI workloads.
  • Guide teams on productionizing PoCs into scalable, resilient, and supportable AI systems.
  • Partner with platform teams to evolve a shared enterprise AI platform.

AWS-Centric AI Architecture
  • Design AI and GenAI solutions using AWS-native services, including (but not limited to):
  • Amazon Bedrock, SageMaker, Lambda, ECS/EKS
  • S3, DynamoDB, Aurora, OpenSearch
  • IAM, KMS, VPC, CloudWatch
  • Define cost, performance, and scalability guardrails for AI workloads on AWS.
  • Ensure architectures follow Well-Architected Framework principles.

Governance, Risk, and Responsible AI
  • Partner with security, legal, and compliance teams to define AI governance, guardrails, and controls.
  • Embed responsible AI principles, data privacy, and explainability into enterprise designs.
  • Establish standards for model access, auditability, and risk management.

Technical Leadership & Influence
  • Act as a principal-level advisor to senior technology and business leaders.
    Champion hands-on experimentation and rapid solution delivery while maintaining technical excellence.
  • Mentor architects and senior engineers on AI architecture and MLOps best practices.
  • Drive alignment across teams by publishing reference architecture, design standards, and decision frameworks.
  • Represent the organization in architecture forums, reviews, and strategic initiatives.

Qualifications
Bachelor's Degree and 10 years of experience in Application Development, Systems Engineering, or Information Technology management OR High School Diploma or GED and 14 years of experience in Application Development, Systems Engineering, or Information Technology management
Preferred Qualifications:
• 10+ years of experience in enterprise architecture, data platforms, or distributed systems.
• Deep expertise in AI/ML architecture, including GenAI and LLM-based systems.
• Strong experience designing MLOps platforms and enterprise AI foundations.
• Proven experience architecting solutions on AWS.
• Experience with Snowflake Cortex AI, Snowflake Native AI capabilities
• Strong understanding of cloud security, networking, and governance.
• Proficiency in Python, SQL, and modern data frameworks (e.g., Databricks, Airflow, Snowflake, Vertex AI).
• Relevant AWS certifications in cloud architecture, AI/ML, generative AI, or related domains.
• Experience with agentic AI design patterns, including tool-use orchestration, autonomous workflow agents, or AI copilots.
• Proficiency in API design, microservices, and containerization (Docker, Kubernetes).
• Demonstrated ability to rapidly prototype new AI concepts and transition successful PoCs into production-grade systems.
#LI-XG1
If hired in NC, the base pay for this position is generally between $160,000 and $240,000. Actual starting base pay will be determined based on skills, experience, location, and other non-discriminatory factors permitted by law. For some roles, total compensation may also include variable incentives, bonuses, benefits, and/or other awards as outlined in the offer of employment.
Benefits are an integral part of total rewards and First Citizens Bank is committed to providing a competitive, thoughtfully designed and quality benefits program to meet the needs of our associates. More information can be found at https://jobs.firstcitizens.com/benefits.

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