MLOps Engineer

Location: Remote / Hybrid
Experience: 2–6 years (or strong project/internship experience)Employment Type: Full-Time Department: AI & Software Systems Key ResponsibilitiesDesign and maintain end-to-end MLOps pipelines: from data ingestion to model deployment and monitoring.Containerize ML models and services using Docker for scalable deployment.Develop and deploy APIs using FastAPI to serve real-time inference for object detection, segmentation, and mapping tasks.Automate workflows using CI/CD tools like GitHub Actions or Jenkins.Manage cloud infrastructure on AWS: EC2, S3, Lambda, SageMaker, CloudWatch, etc.Collaborate with AI and GIS teams to integrate ML outputs into mapping dashboards.Implement model versioning using DVC/Git, and maintain structured experiment tracking using MLflow or Weights & Biases.Ensure secure, scalable, and cost-efficient model hosting and API access. Required SkillsProgramming: Python (must), Bash/Shell scriptingML Frameworks: PyTorch, TensorFlow, OpenCVMLOps Tools: MLflow, DVC, GitHub Actions, Docker (must), Kubernetes (preferred)Cloud Platforms: AWS (EC2, S3, SageMaker, IAM, Lambda)API Development: FastAPI (must), Flask (optional)Data Handling: NumPy, Pandas, GDAL, RasterioMonitoring: Prometheus, Grafana, AWS CloudWatch Preferred ExperienceHands-on with AI/ML models for image segmentation, object detection (YOLOv8, U-Net, Mask R-CNN).Experience with geospatial datasets (satellite imagery, drone footage, LiDAR).Familiarity with PostGIS, QGIS, or spatial database management.Exposure to DevOps principles and container orchestration (Kubernetes/EKS). Soft SkillsProblem-solving mindset with a system design approach.Clear communication across AI, software, and domain teams.Ownership of the full AI deployment lifecycle. EducationBachelor’s or Master’s in Computer Science, Data Science, AI, or equivalent.Certifications in AWS, MLOps, or Docker/Kubernetes (bonus).