Time since we first saw it
Day 2
Early. Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
Work setup in the employer’s posting: TELECOMMUTEFrom the employer’s posting
Design and optimize batch and real-time feature engineering pipelines
Build, deploy and maintain scalable model training and inference pipelines
Develop Spark Streaming solutions and real-time feature generation
Process large volumes of structured and unstructured data
Operate production ML systems and keep their performance consistent
Design and optimize feature engineering pipelines for both batch and real-time workloads
Build, deploy, and maintain scalable model training and inference pipelines
Develop streaming solutions using Spark Streaming
Process structured and unstructured data at scale
Operate and maintain production ML systems to ensure consistent performance
5+ years of experience building, deploying, and operating offline and online feature engineering pipelines
Proficiency in Databricks, Spark/PySpark, Python, and SQL
Expertise in model training and real-time inferencing pipelines
Proven track record of running production-grade ML systems
English proficiency at B2 level or higher
We are seeking a Senior AI Solution Engineer with expertise in Databricks and Machine Learning to design, deploy, and operate production-grade ML systems. The ideal candidate is a hands-on Data Engineer / Machine Learning Engineer with a proven track record of delivering real-time and batch ML solutions, strong capability to build streaming pipelines, and experience ensuring the reliability and scalability of ML systems in production environments. Responsibilities Design and optimize feature engineering pipelines for both batch and real-time workloads Build, deploy, and maintain scalable model training and inference pipelines Process structured and unstructured data at scale Develop streaming solutions using Spark Streaming Enable real-time feature generation and model serving Ensure reliability, scalability, and performance of ML solutions in production Support batch pipelines for model training and retraining Operate and maintain production ML systems to ensure consistent performance Requirements 5+ years of experience building, deploying, and operating offline and online feature engineering pipelines Proficiency in Databricks, Spark/PySpark, Python, and SQL Expertise in model training and real-time inferencing pipelines Experience processing large-scale unstructured data Knowledge of Spark Streaming Experience with real-time feature generation and low-latency model inference Proven track record of running production-grade ML systems English proficiency at B2 level or higher Nice to have Familiarity with MLflow and MLOps practices Experience with AWS Background in Customer Analytics, Recommendation Systems, or Retail
Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 2
Early. Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordCheck the employer’s posting for the current role and application details.
Already applied? Track this application
Yes. We’ve followed one other Senior AI Solution Engineer with Databricks, ML job there since November 2026. It is still up.
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.
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.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
No pay amount identified in the saved description.
Location not supplied.
Working pattern and location restrictions need checking in the full posting.
No clear work-authorization passage found. Eligibility is unconfirmed.
We couldn’t identify enough role detail in this saved description to support an AI answer. Read the full posting