Time since we first saw it
Day 124
Up longer than most. Similar jobs usually come down between day 6 and day 71.
From 4,607 similar postings that came down. Coming down is not always a hire.
Lead ML Ops Engineer
Bangalore North, Karnataka, India
Open 4 months, first seen by us 124 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and build scalable ML infrastructure for training, deployment, monitoring and governance
Develop CI/CD pipelines and automated deployment for ML workflows
Build reliable model serving and inference platforms
Develop feature stores, experiment tracking and model registries
Optimize large-scale data processing pipelines using Apache Spark
From the employer’s posting
Design and build scalable ML infrastructure supporting training, deployment,monitoring, and governance.
Develop CI/CD pipelines for Machine Learning workflows.
Build reliable model serving and inference platforms.
Develop feature stores, experiment tracking, model registries, and automated deployment pipelines.
Optimize large-scale data processing pipelines using Apache Spark.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 124 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
- Jul 5, 2026124 days ago
- Recorded sightings
- 81
- Last seen by us
- Nov 5, 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
PlaySimple Games is hiring
4 open jobs
- No new data jobs since August
- 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.
- Python
- AWS
- Azure
- Docker
- GCP
- Kubernetes
- Spark
Where the posting names them
Job Summary We are looking for a Lead ML Platform / MLOps Engineer to build and scale the infrastructure that powers AI across PlaySimple. You will design, develop, and operate the platforms that enable Data Scientists to rapidly build, deploy, monitor, and continuously improve Machine Learning models in production. You will work closely with Data Science, Platform Engineering, and Product teams to build a world-class AI platform supporting experimentation, personalization, and intelligent decision-making at scale. Responsibilities Design and build scalable ML infrastructure supporting training, deployment,monitoring, and governance. Develop CI/CD pipelines for Machine Learning workflows. Build reliable model serving and inference platforms. Develop feature stores, experiment tracking, model registries, and automated deployment pipelines. Optimize large-scale data processing pipelines using Apache Spark. Build infrastructure supporting LLM applications and Agentic AI workflows. Partner with Data Scientists to productionize Machine Learning models. Improve platform observability, reliability, scalability, and developer productivity. Drive engineering best practices across the ML platform. Requirements Requirements 5–8 years of experience building Machine Learning platforms or MLOps infrastructure. Demonstrated experience deploying and operating production Machine Learning systems. Strong software engineering skills in Python. Experience with Apache Spark and distributed computing. Strong understanding of modern MLOps practices Experience with: o Docker o Kubernetes o ML deployment frameworks o Feature Stores o Model Registries o Experiment Tracking Familiarity with Agentic AI concepts and orchestration platforms such as n8n. Experience with AWS, Azure, or GCP. Product experience in Gaming, Consumer Internet, E-commerce,Marketplace, or Decision Science platforms. Strong communication and collaboration skills. An avid gamer with a passion for understanding player behaviour and game mechanics. What We Look For Strong engineering fundamentals. Platform-first mindset. Excellent problem-solving ability. Ownership and accountability. Curiosity for modern AI technologies. Ability to collaborate across cross-functional teams. Comfortable working in a fast-paced product environment. Passion for gaming.
Your next step
Complete your application on playsimple.zohorecruit.in. The employer’s form will show what is required.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
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
Job Summary We are looking for a Lead ML Platform / MLOps Engineer to build and scale the infrastructure that powers AI across PlaySimple. You will design, develop, and operate the platforms that enable Data Scientists to rapidly build, deploy, monitor, and continuously improve Machine Learning models in production. You will work closely with Data Science, Platform Engineering, and Product teams to build a world-class AI platform supporting experimentation, personalization, and intelligent decision-making at scale. Responsibilities Design and build scalable ML infrastructure supporting training, deployment,monitoring, and governance. Develop CI/CD pipelines for Machine Learning workflows. Build reliable model serving and inference platforms. Develop feature stores, experiment tracking, model registries, and automated deployment pipelines. Optimize large-scale data processing pipelines using Apache Spark. Build infrastructure supporting LLM applications and Agentic AI workflows. Partner with Data Scientists to productionize Machine Learning models. Improve platform observability, reliability, scalability, and developer productivity. Drive engineering best practices across the ML platform. Requirements Requirements 5–8 years of experience building Machine Learning platforms or MLOps infrastructure. Demonstrated experience deploying and operating production Machine Learning systems. Strong software engineering skills in Python. Experience with Apache Spark and distributed computing. Strong understanding of modern MLOps practices Experience with: o Docker o Kubernetes o ML deployment frameworks o Feature Stores o Model Registries o Experiment Tracking Familiarity with Agentic AI concepts and orchestration platforms such as n8n. Experience with AWS, Azure, or GCP. Product experience in Gaming, Consumer Internet, E-commerce,Marketplace, or Decision Science platforms. Strong communication and collaboration skills. An avid gamer with a passion for understanding player behaviour and game mechanics. What We Look For Strong engineering fundamentals. Platform-first mindset. Excellent problem-solving ability. Ownership and accountability. Curiosity for modern AI technologies. Ability to collaborate across cross-functional teams. Comfortable working in a fast-paced product environment. Passion for gaming.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bangalore North, Karnataka, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.