DevOps - 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 a DevOps / Platform Engineer, you will play a central role in building and operating the infrastructure that powers these systems—from data pipelines to AI deployment and real-time operations.

🚀 What You’ll Be Responsible For

Infrastructure Architecture & Deployment

• Design, build, and operate scalable cloud and on-prem infrastructure supporting AI and data systems
• Support hybrid environments combining:
• cloud (AWS / GCP / Azure)
• on-prem GPU clusters
• edge-adjacent compute nodes
• Ensure high availability, fault tolerance, and operational resilience

CI/CD & Continuous Delivery for AI Systems

• Build and maintain CI/CD pipelines for:
• data pipelines
• backend services
• AI/ML models (training → validation → deployment)
• Enable fast, reliable iteration cycles for research and production systems
• Standardize deployment processes across teams

Distributed Systems & Compute Orchestration

• Deploy and manage distributed compute environments, including:
• Kubernetes clusters
• GPU workloads
• Ray or similar distributed execution frameworks
• Optimize resource allocation across:
• CPU / GPU / memory
• batch vs real-time workloads
• Support large-scale data and AI processing pipelines

MLOps & AI Lifecycle Support

• Implement and maintain MLOps workflows, including:
• model training pipelines
• versioning and reproducibility
• deployment and rollback strategies
• monitoring of model performance and drift
• Work closely with AI teams to ensure production readiness of models

Observability, Monitoring & Reliability

• Design and deploy observability systems, including:
• logging
• metrics
• tracing
• Build monitoring dashboards and alerting pipelines
• Diagnose and resolve production issues in distributed systems
• Continuously improve system stability, performance, and uptime

Automation & Platform Engineering

• Automate:
• deployment processes
• scaling strategies
• infrastructure provisioning
• Build internal tools and platforms that enable:
• faster development cycles
• consistent environments
• reproducible pipelines

Cross-Team Collaboration

• Work closely with:
• AI researchers
• data engineers
• backend and systems engineers
• Improve workflows across the full pipeline:
• data → models → deployment → operations
• Act as a bridge between development, infrastructure, and production systems

🎯 What We Are Looking For

Required Experience

• 3+ years of experience as a DevOps / Platform / Infrastructure Engineer
• Proven experience building and operating production-grade systems
• Strong understanding of distributed systems and scalable architectures

Core Technical Skills

• Containers & Orchestration:
• Docker, Kubernetes
• CI/CD Systems:
• GitHub Actions / GitLab CI / Jenkins or similar
• Cloud Platforms:
• AWS / GCP / Azure
• Operating Systems:
• Linux (deep familiarity with system-level behavior)
• Scripting & Automation:
• Python and/or Bash
• Distributed Systems:
• Experience with scalable, high-throughput systems

⭐ Strong Advantages

• Experience with AI / ML systems or MLOps pipelines
• Familiarity with:
• Ray / Airflow / Prefect
• GPU-based workloads and scheduling
• Experience with observability tools:
• Prometheus, Grafana, OpenTelemetry
• Background in data platforms or streaming pipelines
• Experience with real-time or operational systems
• Exposure to defense / aerospace / satellite (VISINT, EO, SAR) domains

🌟 What Awaits You at Algolight

• Ownership over core infrastructure powering national-scale AI systems
• Work on real satellite data and operational intelligence pipelines
• Deep involvement in the intersection of:
• infrastructure × AI × data × real-world systems
• Collaboration with top-tier engineers, researchers, and system architects
• A culture that values:
• engineering depth
• reliability
• real-world impact over hype

🔎 Systems & Data You’ll Work With

• Satellite imagery (EO, SAR)
• Large-scale geospatial datasets
• Distributed AI training and inference systems
• Real-time and batch data pipelines
• Hybrid cloud + on-prem compute environments