Responsibilities

Qualifications

​​ML Infrastructure & Model Deployment

• ML Pipelines Development – Design, build, and maintain scalable ML pipelines to support sensor-agnostic (radar, camera) people-sensing model training, testing, deployment, and monitoring.

• ML Platform Implementation – Develop a scalable ML platform integrating ML pipelines and models, transforming raw sensor data into actionable people-sensing insights.

• Model Deployment – Implement efficient deployment strategies for ML models across on-premise and cloud environments, ensuring real-time inference and seamless scalability.

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Automation, CI/CD & Observability

• End-to-End Pipeline Management – Optimize ML workflows for real-time sensor-driven applications, ensuring robust model lifecycle management.

• CI/CD & Automation – Design and optimize CI/CD pipelines to automate model integration, retraining, validation, and deployment.

• Observability & Performance Monitoring – Implement monitoring tools (e.g., Prometheus, Grafana, Kibana) to track model accuracy, performance, and scaling issues.

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Infrastructure, Security & Scaling

• Containerization & Orchestration – Deploy and scale ML services using Docker and Kubernetes across edge and cloud environments.

• Infrastructure & Security Best Practices – Implement cloud-native best practices for infrastructure automation, logging, alerting, and security monitoring.

Collaboration & Strategic Impact

• Cross-Functional Collaboration – Work closely with data scientists, software engineers, and product teams to define objectives, deliver key milestones, and align solutions with business needs.

• Technology Roadmap Contribution – Play a key role in shaping and executing Algorized’s AI-powered sensing platform roadmap.

Minimum Requirements

• MSc or advanced degree in a relevant field with 5+ years of experience in MLOps, ML model deployment, and cloud infrastructure.

• Strong hands-on experience with deployment and scaling of ML models on on-premise and cloud architectures.

• Expertise in sensor fusion, edge AI, or embedded ML models for real-time applications.

• Knowledge of AI agent architectures or Reinforcement Learning concepts.

• Ability to design efficient communication protocols between front-end and back-end systems.

• API & Communication Protocol Development – Experience with RESTful APIs, WebSockets, GraphQL.

• Database & Real-Time Data Management – Strong understanding of database design, real-time data ingestion, and efficient storage solutions.

• Experience with time-series data, sensor data pipelines, or stream processing frameworks.

• Deep expertise in Docker, Kubernetes, and modern CI/CD practices.

• Strong problem-solving & debugging skills, with the ability to design scalable, efficient systems.

• Excellent teamwork & communication skills, with a passion for innovation and solving complex challenges.

• Willingness to travel domestically and internationally for development and on-site customer support.

Preferred Requirements

• Experience with observability tools (e.g., Grafana, Prometheus, Kibana, Elasticsearch).

• Knowledge of event-driven architectures (e.g., Kafka, MQTT, Pub/Sub).

• Experience in real-time AI model inference & post-deployment monitoring.

• Familiarity with 3D positioning, tracking systems, or vital signs estimation.