En Improving South America, brindamos servicios de TI para transformar la percepción del profesional de TI. Nos enfocamos en consultoría de TI, desarrollo de software y formación ágil.

La empresa promueve una cultura de trabajo excepcional basada en el trabajo en equipo, la excelencia y la diversión, con enfoque en crecimiento personal y recompensas compartidas. Al integrarse, el/la candidato/a formará parte de una comunidad que prioriza la comunicación abierta y relaciones laborales sólidas a largo plazo, respaldada por una estructura de crecimiento profesional y aprendizaje continuo.

Oportunidad laboral en getonbrd.com.

We are looking for an ML Engineer to join our data science and machine learning engineering team. The candidate will be responsible for designing, developing, and deploying advanced machine learning models, with an emphasis on clustering models, time series analysis, and complex heuristics.

Qualifications and requirements

- Strong production Python (3.12+), well beyond scripting-level.
- Snowflake: writing and optimizing SQL, with an understanding of schema, warehouse and role concepts. Key-pair (JWT) authentication experience is a plus.
- Docker: building and optimizing container images, including packaging ML artifacts and their dependencies.
- Kubernetes: deploying and operating workloads (pods, deployments, resource limits, networking basics), with hands-on experience building, scheduling and debugging CronJobs.
- AWS: working knowledge of core services (S3, IAM, boto3). EMR experience is a plus.
- Log- and metrics-based troubleshooting across scheduled jobs and services (Elastic, Datadog or equivalent).




- Testing discipline: pytest fluency and the ability to design meaningful edge-case tests.
- Git/GitHub workflow fluency: feature branching, pull requests, code review.
- Comfort working in a fast-moving agile team and communicating clearly in writing.

Desirable skills

- Model-serving or ML platform experience: KServe, Seldon, BentoML, MLflow or similar.
- Familiarity with common model artifact formats (scikit-learn, PyTorch, ONNX) and their packaging conventions, even if you have not trained the models yourself.
- Helm chart authoring and Terraform (including Datadog monitors and dashboards as code).
- Experience with LLM or agent infrastructure: RAG pipelines, vector stores, agent orchestration, MCP integrations, LLM evaluation harnesses or guardrails. This is the main differentiator for the Copilot side of the team.
- Experience with API gateways and production hardening of AWS-hosted services (for example Kong, secrets management, monitoring).
- Artifact repository publishing (JFrog Artifactory) and Gradle familiarity.
- Claude Code or similar AI coding tools, and spec-driven development.

Conditions

- Contrato a largo plazo.
- 100% Remoto.
- Vacaciones y PTOs
- Posibilidad de recibir 2 bonos al año.
- 2 revisiones salariales al año.
- Clases de inglés.
- Equipamiento Apple.
- Plataforma de cursos en linea
- Budget para compra de libros.
- Budget para compra de materiales de trabajo
- mucho mas..

GETONBRD Job ID: 64476

Política de trabajo remoto

Remoto sólo localmente

El trabajo es 100% remoto, pero los candidatos deben residir en Chile, Argentina, Perú o Colombia para postular.

📌 Machine Learning Engineer (Chile)
🏢 Improving
📍 Chile