Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Senior AI Engineer / Data Scientist
Chandler, Arizona, United States
Open 4 months, first seen by us 130 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
- Not stated
- Employment
Full-time · Contract — employment source
Senior AI Engineer / Data Scientist (Consulting) Location: United States (Remote) Employment Type: Full-Time / Contract Experience Level: Senior About the Role: We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base. Key Responsibilities: * Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions. * MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability. * GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings. * Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark. * Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences. Required Qualifications: * 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory). * 3+ years of experience in a customer-facing consulting or Solutions Architect role. * Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment). * Solid hands-on experience with containerization (Docker) and data pipeline orchestration. * Proven track record of deploying Generative AI and NLP solutions for client applications. * Excellent verbal and written communication skills. Preferred Qualifications: * Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks. * Deep knowledge of large-scale data processing and distributed machine learning techniques. * A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.
Employment type in the employer’s posting: Contract
From the employer’s posting
What you’ll work on
Full postingLead end-to-end ML implementations directly with clients
Design, build and maintain production ML pipelines with CI/CD
Implement and optimize GenAI applications such as LLMs and RAG in production
Manage infrastructure with Docker, orchestrators and Apache Spark
Communicate technical findings and project status to varied audiences
From the employer’s posting
Lead end-to-end ML implementations directly with clients
Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability.
Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings.
Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark.
Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 130 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 27, 2026130 days ago
- Recorded sightings
- 94
- Last seen by us
- Nov 3, 2026
- Employer says posted
- Jun 23, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 130
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
Koantek is hiring
12 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Its jobs tend to stay up a long time
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Databricks
- Docker
- Spark
Where the posting names them
Senior AI Engineer / Data Scientist (Consulting) Location: United States (Remote) Employment Type: Full-Time / Contract Experience Level: Senior About the Role: We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base. Key Responsibilities: * Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions. * MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability. * GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings. * Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark. * Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences. Required Qualifications: * 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory). * 3+ years of experience in a customer-facing consulting or Solutions Architect role. * Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment). * Solid hands-on experience with containerization (Docker) and data pipeline orchestration. * Proven track record of deploying Generative AI and NLP solutions for client applications. * Excellent verbal and written communication skills. Preferred Qualifications: * Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks. * Deep knowledge of large-scale data processing and distributed machine learning techniques. * A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.
Your next step
Complete your application on koantek.zohorecruit.com. The employer’s form will show what is required.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
Senior AI Engineer / Data Scientist (Consulting) Location: United States (Remote) Employment Type: Full-Time / Contract Experience Level: Senior About the Role: We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base. Key Responsibilities: * Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions. * MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability. * GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings. * Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark. * Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences. Required Qualifications: * 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory). * 3+ years of experience in a customer-facing consulting or Solutions Architect role. * Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment). * Solid hands-on experience with containerization (Docker) and data pipeline orchestration. * Proven track record of deploying Generative AI and NLP solutions for client applications. * Excellent verbal and written communication skills. Preferred Qualifications: * Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks. * Deep knowledge of large-scale data processing and distributed machine learning techniques. * A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.
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Chandler, Arizona, United States
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