Time since we first saw it
Day 153
Up longer than most. Similar jobs usually come down between day 6 and day 70.
From 4,452 similar postings that came down. Coming down is not always a hire.
Machine Learning Engineer with an Agentic Focus
Location not stated
Open 5 months, first seen by us 153 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Contract — employment source
Employment type in the employer’s posting: Contractor
From the employer’s posting
What you’ll work on
Full postingDesign and deploy ML models and solutions using LangGraph and MLflow
Build and maintain data and feature pipelines for real-time and batch ML work
Monitor models for drift, bias and performance degradation
Optimize ML inference for lower latency and cost
Troubleshoot complex issues in ML models, data pipelines and production AI systems
From the employer’s posting
Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow
Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing
Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation
Optimize ML model inference performance to improve latency and cost-efficiency of AI applications.
Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI system.
Posting record
Report an errorOpen 5 monthsWe first saw this posting 153 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 4, 2026153 days ago
- Recorded sightings
- 74
- 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
High 5 Games is hiring
2 open jobs
- No new data jobs since June
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
- BigQuery
- Docker
- GCP
- Kubernetes
- MLflow
- Airflow
- PyTorch
- scikit-learn
- TensorFlow
Where the posting names them
- Understanding of model monitoring, logging, and observability best practices for ML models and applications. - Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Familiarity with AI orchestration concepts using tools like LangGraph or LangChain is a bonus.
- Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. - Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference. - Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards.
- 1+ years of experience as an ML Engineer, with a focus on developing and deploying machine learning models in production environments. - Strong experience in Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow). - Solid grasp of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services.
- Strong experience in Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow). - Solid grasp of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services. - Experience building and deploying scalable data pipelines and machine learning models in production environments.
We are looking for a Machine Leaning Engineer (MLE) to design, build, and optimize our machine learning operation. You will play a crucial role in scaling AI models from research to production, ensuring smooth model deployment, monitoring, and lifecycle management across our Google Cloud Platform (GCP) infrastructure. You'll work closely with data scientists, ML Ops, and data engineers to automate workflows, improve model performance, and ensure reliability for our AI that serves millions of players worldwide. What You'll Do
What You'll Do - Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. - Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference.
Your next step
Complete your application on high5games.bamboohr.com. The employer’s form will show what is required.
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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
We are looking for a Machine Leaning Engineer (MLE) to design, build, and optimize our machine learning operation. You will play a crucial role in scaling AI models from research to production, ensuring smooth model deployment, monitoring, and lifecycle management across our Google Cloud Platform (GCP) infrastructure. You'll work closely with data scientists, ML Ops, and data engineers to automate workflows, improve model performance, and ensure reliability for our AI that serves millions of players worldwide.
What You'll Do
- Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management.
- Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference.
- Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards.
- Optimize ML model inference performance to improve latency and cost-efficiency of AI applications.
- Ensure the overall reliability, performance, and scalability of the ML models and data infrastructure platform, including proactive identification and resolution of issues related to model performance and data quality.
- Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI system.
- Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming.
What We're Looking For
- 1+ years of experience as an ML Engineer, with a focus on developing and deploying machine learning models in production environments.
- Strong experience in Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow).
- Solid grasp of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services.
- Experience building and deploying scalable data pipelines and machine learning models in production environments.
- Understanding of model monitoring, logging, and observability best practices for ML models and applications.
- Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Familiarity with AI orchestration concepts using tools like LangGraph or LangChain is a bonus.
- Bonus experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.
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