Colombia onlyYou’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.
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