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 toolingEstablish 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.