Machine Learning Engineer with an Agentic Focus

High 5 Games·Remote(India)
AI & Machine Learning

WFA Digital Insight

As the demand for AI and machine learning specialists continues to rise, with a notable 27% growth in the past year, professionals with expertise in deploying and managing AI models are in high demand. High 5 Games, a leader in digital entertainment, is looking for a skilled Machine Learning Engineer to join their team. This role stands out for its focus on scaling AI models and ensuring their reliability and performance. Candidates should be prepared to showcase their experience with Google Cloud Platform, containerization, and model monitoring. Before applying, it's crucial to understand the complexities of working in a real-money gaming environment and the importance of data governance and security.

Job Description

About the Role

The Machine Learning Engineer position at High 5 Games is a critical role that involves designing, building, and optimizing machine learning operations. The successful candidate will play a pivotal role in scaling AI models from research to production, ensuring smooth deployment, monitoring, and lifecycle management across the company's Google Cloud Platform (GCP) infrastructure. This role is part of a collaborative team effort, working closely with data scientists, ML Ops, and data engineers to automate workflows, improve model performance, and ensure the reliability of AI systems that serve millions of players worldwide.

The day-to-day activities of this role will involve a deep dive into the technical aspects of machine learning model development and deployment. The engineer will be responsible for leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management, ensuring that models are not only deployed efficiently but also continuously monitored for performance and potential biases.

Given the nature of High 5 Games' business, which involves real-money gaming, the Machine Learning Engineer will need to have a keen understanding of and adherence to data governance, security, and compliance requirements. This includes ensuring that all AI/ML models and workflows meet the necessary standards for real-money gaming, a sector that is heavily regulated and sensitive to ethical considerations.

What You Will Do

  • Design, develop, and deploy machine learning models and solutions that meet the company's needs for predictive analytics and automation.
  • Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real-time and batch processing.
  • Utilize 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.
  • Leverage tools like Vertex AI Model Monitoring and custom dashboards for monitoring and observability purposes.
  • 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.
  • Proactively identify and resolve issues related to model performance and data quality.
  • Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI systems.
  • Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming.

What We Are 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 plus.
  • Bonus experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.

Nice to Have

  • Experience with real-money gaming or similar regulated industries.
  • Knowledge of additional ML frameworks and tools beyond the basics.
  • Participation in open-source ML projects or personal ML projects.
  • Certification in GCP or ML technologies.

Benefits and Perks

  • The opportunity to work with a leading digital entertainment company.
  • Competitive compensation package tailored to the candidate's experience.
  • Health insurance and other benefits that support work-life balance.
  • Remote work stipend to ensure a comfortable and efficient work environment.
  • Professional development opportunities, including training and conference sponsorships.
  • Access to the latest technologies and tools in the ML field.
  • Collaborative and dynamic work environment with a team of skilled professionals.

How to Stand Out

  • Be prepared to showcase your experience with Google Cloud Platform and machine learning model deployment, with specific examples from your past work.
  • Highlight your understanding of data governance, security, and compliance, especially in regulated industries like real-money gaming.
  • Emphasize your ability to work in a collaborative environment, including experience with Agile methodologies and version control systems like Git.
  • Make sure your portfolio includes examples of machine learning models you've deployed and managed, including any metrics on their performance and your role in their development.
  • Practice explaining complex technical concepts simply, as you will be working with both technical and non-technical stakeholders.

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