Principal Machine Learning Engineer
Core
Design, build, and maintain scalable MLOps, AIOps, and GenAI infrastructure to enable Data Science teams to deliver production-grade ML solutions.
Role type
Principal Machine Learning Engineer (MLOps & GenAI Platform)
Builds
Production-grade ML platforms, MLOps frameworks, GenAI/LLM solutions, and reusable engineering templates.
Domain
Online gaming, betting, and interactive entertainment
Deliverable
production ML models | infrastructure
Required skills
MLOps platform design, AWS cloud services, Snowflake data platform, workflow orchestration (Prefect/Airflow), Infrastructure as Code (IaC), CI/CD pipelines, Python engineering, containerization (Docker/Kubernetes), ML lifecycle management, GenAI/LLM implementation, technical leadership, architectural standards definition.
Preferred skills
AWS SageMaker/Bedrock, MLflow, feature stores, model observability, AIOps use cases, internal developer platforms, secure GenAI patterns, regulated high-scale environments.
Technologies
AWS, Snowflake, Prefect, Airflow, Docker, Kubernetes, ECS, EKS, Python, CI/CD tools, MLflow, EventBridge, CloudWatch, S3, ECR, IAM, Bedrock, SageMaker, Lambda, Step Functions
Responsibilities
Lead design and implementation of scalable MLOps and AIOps frameworks; Design and maintain reusable ML infrastructure components; Provide technical leadership across ML engineering initiatives; Support development of ML platform capabilities covering experimentation, training, orchestration, deployment, and monitoring; Collaborate with Data Scientists and stakeholders to translate requirements into technical solutions; Contribute to GenAI and LLM-based solutions including enterprise AI assistants and agentic workflows; Mentor engineers and promote high engineering standards; Identify operational risks and propose pragmatic improvements.
Seniority
Principal, hands-on IC with strategic influence and mentorship