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Senior AI Engineer, Databricks, ML, Spark ML
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingBuild, test, and deploy ML and GenAI components on Databricks
Design and implement anomaly-detection algorithms for industrial diagnostics
Create RAG pipelines to support intelligent information retrieval
Implement document extraction workflows for structured and unstructured data
Monitor deployed models and iterate based on performance metrics and feedback
From the employer’s posting
Build, test, and deploy ML and GenAI components on Databricks
Design and implement anomaly-detection algorithms for industrial diagnostics
Create RAG pipelines to support intelligent information retrieval
Implement document extraction workflows for structured and unstructured data
Monitor deployed models and iterate based on performance metrics and feedback
What you’ll bring
All qualificationsCore experience
- 3+ years of experience in AI/ML engineering and solution delivery
- Proficiency in Databricks, Spark ML, and distributed data processing
- Background in developing predictive models and anomaly-detection algorithms
- Skills in building RAG pipelines, document extraction, and AI agents
- Upper-Intermediate English (B2) or higher
Qualification wording
3+ years of experience in AI/ML engineering and solution delivery
Proficiency in Databricks, Spark ML, and distributed data processing
Background in developing predictive models and anomaly-detection algorithms
Skills in building RAG pipelines, document extraction, and AI agents
Proficiency in English at an Upper-Intermediate level (B2) or higher
Tools in this posting
- Databricks
Source — Tool mentions in context
We are seeking a Senior AI Engineer to build, test, and deploy ML and GenAI components for asset onboarding, industrial diagnostics, and device analytics on Databricks. The specialist will develop predictive models, anomaly-detection algorithms, RAG pipelines, document extraction, and AI agent capabilities. Responsibilities Build, test, and deploy ML and GenAI components on Databricks Develop predictive models for asset onboarding and device analytics Design and implement anomaly-detection algorithms for industrial diagnostics Create RAG pipelines to support intelligent information retrieval Implement document extraction workflows for structured and unstructured data Develop AI agent capabilities to enable advanced automation scenarios Collaborate with cross-functional teams to translate business needs into AI solutions Optimize model performance, scalability, and reliability in production Monitor deployed models and iterate based on performance metrics and feedback Requirements 3+ years of experience in AI/ML engineering and solution delivery Expertise in AI Solution Engineering, including model design and deployment Proficiency in Databricks, Spark ML, and distributed data processing Background in developing predictive models and anomaly-detection algorithms Skills in building RAG pipelines, document extraction, and AI agents Understanding of GenAI concepts and industrial diagnostics use cases Capability to design end-to-end ML workflows from data ingestion to deployment Proficiency in English at an Upper-Intermediate level (B2) or higher
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 27, 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
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordYour next step
Check the employer’s posting for the current role and application details.
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Has EPAM Systems posted this job before?
Yes. We’ve followed 3 other Senior AI Engineer, Databricks, ML, Spark ML jobs there since November 2026. All 3 are still up.
Large employers often post one title for several teams, so these can be separate openings.
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
We are seeking a Senior AI Engineer to build, test, and deploy ML and GenAI components for asset onboarding, industrial diagnostics, and device analytics on Databricks. The specialist will develop predictive models, anomaly-detection algorithms, RAG pipelines, document extraction, and AI agent capabilities. Responsibilities Build, test, and deploy ML and GenAI components on Databricks Develop predictive models for asset onboarding and device analytics Design and implement anomaly-detection algorithms for industrial diagnostics Create RAG pipelines to support intelligent information retrieval Implement document extraction workflows for structured and unstructured data Develop AI agent capabilities to enable advanced automation scenarios Collaborate with cross-functional teams to translate business needs into AI solutions Optimize model performance, scalability, and reliability in production Monitor deployed models and iterate based on performance metrics and feedback Requirements 3+ years of experience in AI/ML engineering and solution delivery Expertise in AI Solution Engineering, including model design and deployment Proficiency in Databricks, Spark ML, and distributed data processing Background in developing predictive models and anomaly-detection algorithms Skills in building RAG pipelines, document extraction, and AI agents Understanding of GenAI concepts and industrial diagnostics use cases Capability to design end-to-end ML workflows from data ingestion to deployment Proficiency in English at an Upper-Intermediate level (B2) or higher
Source details
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