MLOps - AI & Data, Satellite Team

At Algolight, we live and breathe the future of artificial intelligence and the physical world.

Our mission it two-folder:

On the civilian side, we build labeled 3D information layers from all types of sensors—for smart cities, drones, autonomous vehicles, infrastructure, public safety, and far beyond.

On the defense side, we bring true real-time intelligence to the edge—anywhere, for any sensor, at any point on the map—enabling smart, real-time decisions in the field.

VISINT & Multi-Sensor Intelligence Platform

At Algolight, we are building large-scale AI systems that transform satellite and aerial data (EO, SAR, and beyond) into actionable intelligence.

These systems span:

• Massive data ingestion pipelines from spaceborne and airborne sensors
• Advanced AI/ML pipelines for detection, change analysis, and scene understanding
• Distributed compute environments for training, inference, and analytics
• Operational systems deployed in real-world, mission-critical environments

As an MLOps Engineer, you will be responsible for turning AI models into reliable, scalable, production-grade systems.

🚀 What You’ll Be Responsible For

End-to-End MLOps Pipelines

• Design and build end-to-end MLOps workflows:
• Data → Training → Evaluation → Deployment → Monitoring
• Ensure reproducibility, scalability, and reliability across the full model lifecycle
• Standardize pipelines across teams and projects

Training Pipelines & Experimentation

• Build and maintain training pipelines for large-scale AI models
• Enable:
• experiment tracking
• comparison of model variants
• reproducible training workflows
• Support research teams in moving from experiments to stable pipelines

Model Management & Versioning

• Implement and manage:
• model registries
• versioning systems
• artifact management pipelines
• Work with tools such as:
• MLflow / W&B / DVC
• Ensure full traceability of:
• data → model → evaluation → deployment

Model Deployment (Production Systems)

• Deploy models into production environments, including:
• batch pipelines
• real-time inference systems
• Support deployment across:
• cloud
• on-prem
• edge-adjacent environments
• Ensure reliability, rollback capability, and performance stability

Distributed Training & Inference

• Work with distributed systems for large-scale training and inference:
• Kubernetes
• Ray or similar frameworks
• Optimize workloads across:
• CPU / GPU
• memory / throughput
• Enable efficient scaling of AI systems

Monitoring, Drift & Continuous Improvement

• Build systems for model monitoring, including:
• performance metrics
• data drift
• data quality
• Enable:
• continuous evaluation
• automated retraining pipelines
• Ensure models remain robust under changing real-world conditions

Automation & Performance Optimization

• Automate:
• retraining workflows
• evaluation pipelines
• deployment processes
• Optimize:
• training efficiency
• inference latency
• infrastructure cost (CPU/GPU utilization)

Cross-Team Collaboration

• Work closely with:
• AI researchers
• data engineers
• DevOps / platform engineers
• Bridge the gap between:
• research → production → operational systems
• Ensure models are built with deployment and real-world usage in mind

🎯 What We Are Looking For

Required Experience

• 3+ years in MLOps / ML Engineering / Data Engineering (ML-focused)
• Proven experience building ML pipelines in production environments
• Strong understanding of the machine learning lifecycle

Core Technical Skills

• Programming:
• Python (mandatory)
• ML Frameworks:
• PyTorch / TensorFlow
• Pipelines & Experimentation:
• Training pipelines, experiment tracking (MLflow / W&B)
• Containers & Orchestration:
• Docker, Kubernetes
• Cloud Platforms:
• AWS / GCP / Azure
• ML Systems Thinking:
• Understanding of challenges in moving models from research to production

⭐ Strong Advantages

• Experience with satellite data (EO / SAR) or geospatial AI
• Familiarity with:
• Ray / Airflow / Prefect
• Experience with GPU workloads and training optimization
• Experience with:
• data versioning (DVC)
• feature stores
• Experience with observability tools:
• Prometheus, Grafana, OpenTelemetry
• Background in real-time or operational systems
• Experience in defense / aerospace / large-scale AI systems

🌟 What Awaits You at Algolight

• Ownership over model lifecycle infrastructure for operational AI systems
• Work on real-world multi-sensor data at scale (including satellite data)
• Deep involvement in:
• AI × data × infrastructure × deployment systems
• Collaboration with top-tier AI researchers and system engineers
• A culture that values:
• robustness
• reproducibility
• real-world impact over experimentation alone

🔎 Systems & Data You’ll Work With

• Satellite imagery (EO, SAR)
• Multi-sensor datasets (vision, radar, geospatial)
• Large-scale training pipelines
• Distributed inference systems
• Real-time and batch AI deployments