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.