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Feeding Ai Jobs (NOW HIRING)

1 point system

GCP Data Engineer

Bentonville, AR · On-site

$100K - $120K/yr

Build and maintain data pipelines feeding AI/ML feature stores and forecasting models * Collaborate with AI Developers to ensure high-quality, low-latency data access for model training * Manage and ...

1 point system

GCP Data Engineer

Bentonville, AR · On-site

$100K - $120K/yr

Build and maintain data pipelines feeding AI/ML feature stores and forecasting models * Collaborate with AI Developers to ensure high-quality, low-latency data access for model training * Manage and ...

Citigroup, Inc.

Lead Generative AI Developer

New York, NY · On-site

$176K - $265K/yr

Design and optimize data pipelines feeding AI systems, working with vector databases (e.g., Pinecone, Weaviate, pgvector) and enterprise data platforms. * Technical Leadership: Mentor junior ...

OperAxis

Data Engineer

Bentonville, AR · On-site

$97K - $117K/yr

... feeding AI/ML feature stores and forecasting models • Collaborate with AI Developers to ensure high-quality, low-latency data access for model training • Manage and optimize Cloud Composer DAGs ...

DataHub

Senior Platform Engineer

Palo Alto, CA · Hybrid

$225K - $300K/yr

No unified way to track the complex data supply chain feeding AI systems * Engineering teams struggling with data discovery, lineage, and governance * Organizations needing machine-scale metadata ...

DataHub

Staff, Backend Engineer - Catalog

Palo Alto, CA

$225K - $300K/yr

No unified way to track the complex data supply chain feeding AI systems * Engineering teams struggling with data discovery, lineage, and governance * Organizations needing machine-scale metadata ...

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How much do feeding ai jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for feeding ai in the United States is $12.36, according to ZipRecruiter salary data. Most workers in this role earn between $9.62 and $14.42 per hour, depending on experience, location, and employer.

What is a Feeding AI?

Feeding AI jobs generally refer to roles involved in providing, curating, and managing the data that artificial intelligence systems use for training and learning. This can include tasks such as data annotation, data labeling, data collection, and preprocessing to ensure AI models are exposed to accurate and relevant information. These jobs are crucial because the quality and variety of data directly influence how well an AI system performs. Feeding AI jobs may be found in industries like tech, healthcare, automotive, and more, supporting the development of smarter, more reliable AI solutions.

What are some typical challenges faced by Feeding AI specialists when curating and preparing data for machine learning models?

Feeding AI specialists often encounter challenges such as ensuring data quality, eliminating biases, and managing large, unstructured datasets. A key part of the role involves collaborating with data scientists and engineers to understand the specific requirements of machine learning models, then sourcing, cleaning, and labeling data accordingly. Balancing data privacy and compliance while maintaining dataset diversity is also common. Teamwork and strong communication skills are essential, as specialists frequently coordinate with cross-functional teams to align on project goals and timelines.

What are the key skills and qualifications needed to thrive as a Feeding AI engineer, and why are they important?

To thrive as a Feeding AI Engineer, you need strong programming skills (especially in Python), a solid understanding of machine learning principles, and a degree in computer science or a related field. Experience with AI frameworks such as TensorFlow or PyTorch, and familiarity with data pipelines and cloud-based systems, are typically required. Analytical thinking, problem-solving ability, and effective communication make someone stand out in this position. These skills ensure the efficient development, training, and optimization of AI models that drive innovation and deliver accurate results.

What is the difference between Feeding Ai vs Data Annotator?

AspectFeeding AiData Annotator
Required CredentialsBasic technical skills, sometimes certifications in AI or data handlingOften high school diploma or equivalent; training in annotation tools
Work EnvironmentRemote or office-based, collaborative with AI teamsPrimarily remote or on-site, focused on labeling data
Industry UsageAI development, machine learning projectsData preparation for AI, machine learning, and analytics
Search & Comparison IntentUnderstanding roles in AI data pipelineClarifying data labeling and annotation tasks

Feeding Ai involves preparing and managing data for AI systems, often requiring technical skills and collaboration with AI teams. Data Annotator focuses on labeling and annotating data to train machine learning models, typically with less technical credentials. Both roles are essential in AI development but differ in scope and responsibilities.

More about Feeding Ai jobs

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What states have the most Feeding Ai jobs?

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What job categories do people searching Feeding Ai jobs look for?

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Feeding Ai jobs near you
Infographic showing various Feeding Ai job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution, with an average salary of $25,703 per year, or $12.4 per hour.

Principal AI Architect - Supply Chain

Stellantis

Auburn Hills, MI • On-site

Full-time

Posted 7 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 131 frontline employees who took The Breakroom Quiz

14th of 44 rated automakers

Job description

We're building an AI-enabled supply chain that senses, predicts, prescribes, and acts. As Principal AI Architect, you'll design and build enterprise-grade AI systems - from data pipelines and models to agents and applications - that run in production across our Supply Chain organization.
This is a hands-on technical role. You'll be in the architecture, in the code, and in the weeds of production systems. You'll design solutions, prototype approaches, write and review code, and unblock engineering teams building alongside you.
Responsibilities include but not limited to:
  • Architect and help build AI, generative AI, and agentic AI solutions - from proof of concept through production - using Python, FastAPI, PyTorch, LangGraph, and AutoGen
  • Design solution architecture across data pipelines (Snowflake, Databricks), models, agents, RAG pipelines, vector databases, APIs, and React-based applications on AWS and Azure
  • Get hands-on with complex technical problems: debugging production issues, prototyping new approaches, and reviewing code and system design
  • Lead architecture reviews and technical decision-making, weighing tradeoffs across performance, cost, scalability, and maintainability
  • Use GitHub, GitHub Actions, and GitHub Copilot to build and ship faster - for your own work and across teams
  • Partner directly with product, data engineering, AI engineering, and platform teams to solve real technical problems, not just review their plans
  • Mentor engineers through pairing, code review, and hands-on problem-solving

Basic Qualifications:
  • Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related technical field
  • 8+ years building production software, AI/ML systems, or data platforms
  • 5+ years architecting and building production AI/ML solutions in Python, including LLMs, RAG architectures, and vector databases, on cloud-native infrastructure (AWS or Azure), with at least 1+ years of hands-on experience in agentic AI frameworks (e.g., LangGraph, AutoGen, or equivalent)
  • Experience with modern MLOps practices and CI/CD (GitHub Actions or equivalent)
  • Proven ability to take AI or software systems from concept through production, including debugging, performance tuning, and operational support
  • Comfortable operating independently and making architecture calls with incomplete information
  • Strong communication skills - able to explain technical tradeoffs to engineers, product partners, and senior leaders

Preferred Qualifications:
  • Experience with FastAPI or similar frameworks for building production AI/ML services
  • Experience with PyTorch for model development, fine-tuning, or inference
  • Experience with Snowflake and/or Databricks for data pipelines feeding AI/ML systems

What Stellantis employees say

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