Senior Machine Learning Engineer

MLOps & Infrastructure

• You are focused on building robust, scalable, and reproducible ML workflows;
• You’ve implemented CI/CD pipelines for ML systems with model versioning, automated evaluation, and deployment hooks;
• You deploy and maintain infrastructure as code using Terraform, provisioning ML workloads on GCP and Kubernetes;
• You’ve built serving systems enabling real-time inference for latency-critical applications;
• Your pipelines are observable and production-grade—wired into Prometheus, Grafana, and on-call tools like incident.io.

Architecture

• You’ve played a key role in architecting mature ML systems that deliver real business value—from real-time inference platforms to scalable retraining pipelines;
• You’ve led technical design and execution of system-wide improvements, such as migrating legacy workflows to modular, containerized, and versioned ML pipelines;
• You’ve run PoCs for new tools and frameworks (e.g., Seldon, LLM integration), evaluating them across scalability, maintainability, and performance dimensions;
• You’ve built and designed a platform that enables data science effectiveness based on ML lifecycle needs,  timely product deliveries and cross-functional stakeholder goals.

Machine Learning

• You work closely with data scientists and researchers, and you know how to translate prototypes into reliable production services;
• You’ve built and maintained models for classification, ranking, and embedding-based retrieval in PyTorch, with thoughtful evaluation and data validation workflows;
• You’ve integrated LLMs into systems—from prompt engineering to fine-tuning to serving—and understand their behavior in production;
• You care about model monitoring, drift detection, and making sure performance doesn’t degrade silently over time.

Engineering Hands-On

• You’ve supported the full lifecycle of ML —from experimentation to production deployment leveraging state of the art MLOps tools (e.g., MLFlow, Feast, Ray..io, Evidently.ai);
• You’ve built resilient batch pipelines with Airflow and dbt, and streaming data infrastructure with Kafka to support near real-time use cases;
• You use Docker, Kubernetes, and FastAPI to build, containerize, and expose model services in production;
• You’re a master in Python, and apply the very best software engineering practices to pipeline design, testing, and performance optimization for scalable ML systems.

Tech Leadership

• You’ve served as a leader in technology in the past; you’ve made mistakes and learned from them;
• You have interacted with a large community of stakeholders before; you understand the business use cases and can tailor your communication to ICs and Senior Management.

Traits

• You like to have a good time while getting things done. When we say a “team player” we mean it - you have a crisp high-five and funny stories to tell. You have your team’s back, and the team has yours;
• You love learning new things: You know that there’s always more to learn. You’re up-to-date on new trends in data – you know who’s using what to solve various problems and are excited for the next release of your favorite tool;
• Past experience in the Esports/Gaming/Betting/Events industry would be a great asset;
• You make the time to cheer and enjoy the ride.