At EFG (ESL FACEIT Group), we create worlds beyond gameplay, where players and fans become a community.
We pride ourselves on our corporate social responsibility: "IT'S NOT GG, UNTIL IT'S GG FOR ALL."
Our passion, craft, and DNA align to shape the world of esports, gaming tournaments, leagues, events, and ecosystems through our community of players, fans, heroes, and our culture.
We are seeking an experienced Senior Machine Learning Engineer who values kindness as a mark of intelligence and understands the responsibility to uplift those around them.
Join the ML Ops Team, tasked with designing, building, and evolving a top-tier ML Ops platform that empowers Data Scientists and benefits EFG's Business teams.
This role offers a unique chance to influence the architecture and technical patterns of a new ecosystem.
Serve as a leader in tech Ask all the whys to understand customer needs deeply and ensure solution alignment; Partner with stakeholders, acting as an internal consultant to promote data platform adoption; Contribute to the team's technical strategy, prioritization, and delivery; Maintain high standards for documentation, testing, resiliency, monitoring, and code quality; Seek efficiencies and simplify code, infrastructure, and data models; Inspire, teach, and guide team members; lead design sessions; oversee code reviews and operational processes.
Excel as Senior Engineer Write well-documented, reusable code that captures core solutions; Break down ambiguous problems into multi-tool solutions; Design complex architectures integrating multiple services and SaaS tools, leveraging GCP expertise; Drive cost efficiencies and address tech debt regularly.
Personify our DNA Exemplify our values, foster a blameless culture, and care for team members; Build strong relationships, being a heart-first, people-first tech lead.
Requirements MLOps & Infrastructure Build scalable, reproducible ML workflows; Implement CI/CD pipelines with model versioning and automated evaluation; Manage infrastructure as code with Terraform on GCP and Kubernetes; Deploy real-time inference systems for latency-critical applications; Ensure observability with Prometheus, Grafana, and incident management tools; Architect mature ML systems delivering real business value; Lead system improvements and evaluate new tools/frameworks; Build platforms supporting data science lifecycle and stakeholder goals; Collaborate closely with data scientists, translating prototypes into production; Maintain models in PyTorch, including evaluation and validation workflows; Integrate LLMs and monitor their performance; Support full ML lifecycle with tools like MLFlow, Feast, Ray.io, Evidently.ai; Develop resilient batch and streaming pipelines with Airflow, dbt, Pub/Sub; Utilize Docker, Kubernetes, FastAPI for deployment; Apply best practices in Python for scalable ML systems.
Tech Leadership Have prior leadership experience, learn from mistakes; Engage with diverse stakeholders, understanding business use cases; Be a team player, fostering a positive environment; Stay updated with industry trends and tools; Experience in esports, gaming, betting, or events industry is a plus; Enjoy the journey and celebrate successes.
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