Senior Machine Learning Engineer

You’ll build and run large\-scale data, ML, and agentic systems. The focus is geospatial pipelines, operational ML, and modern agent frameworks. You should be comfortable owning the full lifecycle: data ingestion, distributed processing, model development, deployment, and monitoring. **Key Responsibilities:** * Implement and integrate agent\-based systems into operational workflows. * Build, deploy, and monitor ML/AI models in production (batch). * Design, build, and maintain large\-scale geospatial data pipelines. * Develop backend services and ML tooling * Establish observability for pipelines, models, and agents (metrics, tracing, alerting). * Collaborate with product and customer teams to drive revenue. **Requirements** * Strong experience with distributed data processing (Spark, Python, Scala). * Strong experience building production ML systems (training, deployment, monitoring). * Experience with agent frameworks (LangChain, OpenAI Assistants, custom agentic architectures). * Experience with AWS across data, compute, and ML services. * Proficiency with CI/CD, infrastructure as code, containerization. **Nice to Have:** * Experience with large geospatial datasets, formats, and indexing strategies. * Experience with vector databases, search, or embeddings. * Experience with graph or spatial databases. * Experience with fine tuning LLM models. **What Success Looks Like:** * Reliable, scalable geospatial pipelines running in production. * ML/AI models deployed with robust monitoring, automated retraining, and clear visibility. * Agentic workflows improving internal/external operations. * Infrastructure that is stable, observable, and automated.