Senior ML Platform Engineer

Our entertainment client is seeking a Senior Platform Engineer to help build and support the infrastructure that powers Real Time computer vision and machine learning systems used across connected devices, cameras, and large-scale digital experiences. This is NOT a Data Scientist, AI Research, or Prompt Engineering role. The focus is on platform engineering, cloud infrastructure, Kubernetes, distributed systems, and production support for machine learning applications. The ideal candidate is someone who builds and supports the platform that enables machine learning systems to run reliably in production rather than someone primarily focused on developing ML models., Platform Engineering:

• Build and support production platforms that power computer vision and machine learning workloads
• Design and maintain scalable Kubernetes-based infrastructure supporting Real Time systems
• Develop automation, tooling, and services that improve reliability and operational efficiency

Production Support:

• Troubleshoot complex production issues across infrastructure, applications, networking, and data pipelines
• Design highly available, observable, resilient, and scalable systems
• Monitor system health and optimize platform performance

Real-Time Systems:

• Design and support event-driven architectures and Real Time processing pipelines
• Build and maintain microservices supporting distributed applications
• Support high-throughput messaging technologies such as Kafka, Kinesis, Redis, or similar platforms

Cloud & Infrastructure:

• Develop and maintain cloud-native solutions in AWS
• Build and support containerized applications using Docker and Kubernetes
• Maintain Linux/Ubuntu-based environments

Machine Learning Infrastructure:

• Partner with computer vision and machine learning teams to deploy and operationalize models
• Support inference pipelines and ML workloads running in production
• Assist with deployment, monitoring, and scaling of ML-enabled applications

Platform Engineering:

• 3+ years of software engineering or platform engineering experience
• Strong Kubernetes experience
• Strong Docker/containerization experience
• Experience supporting production distributed systems
• Experience building and supporting microservices architectures

Cloud & Development:

• Strong AWS experience
• Strong Python development experience
• Experience working in Linux/Ubuntu environments

Real-Time Systems:

• Experience building or supporting real-time/streaming data pipelines
• Strong understanding of event-driven architectures
• Experience with Kafka, Kinesis, Redis, RabbitMQ, Pulsar, or similar messaging technologies

Operations & Troubleshooting:

• Proven production troubleshooting experience
• Strong understanding of observability, monitoring, logging, and system reliability
• Ability to independently investigate and solve complex technical problems

Machine Learning Awareness:

• Experience supporting machine learning or computer vision systems in production environments
• Understanding of inference pipelines and ML deployment workflows

Preferred Skills

• Experience supporting computer vision applications
• Exposure to object detection or inference pipelines
• Experience with MLOps environments
• AWS SageMaker
• Experience with YOLO, OWL, SAM, VLMs, or similar computer vision models
• Stream processing frameworks such as Spark Streaming, Kafka Streams, or Flink
• Experience supporting high-visibility, performance-critical systems
• Experience handling sensitive data with strong security and access controls
• Java experience

Required Education Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related technical field

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