Description :

We're hiring an MLOps Engineer to operationalize ML/LLM features end-to-end-from pipelines and deployment to monitoring, reliability, and cost control. You'll work closely with backend engineers and data/ML teams to take models from experimentation to stable production systems with strong automation and observability.

Responsibilities :

- Build and maintain automated ML/LLM pipelines (training, evaluation, packaging, deployment, rollback).

- Design production model serving and scaling; optimize latency, throughput, and inference cost.

- Implement monitoring across system, data, and model layers; detect drift and performance degradation, and drive fixes/retraining.

- Improve reliability with CI/CD, testing, alerting, and incident response runbooks.

- Establish versioning and governance for code, data, and models to ensure reproducibility and auditability.

Must-have skills :

- Strong Python and software engineering fundamentals; experience deploying services/APIs.

- Hands-on MLOps : CI/CD for ML, model/data versioning, monitoring/observability, drift management.

- Cloud + containers (AWS/GCP/Azure), Docker; orchestration experience is a plus.

Nice-to-have :

- ML tooling exposure (e.g., MLflow/Kubeflow/feature stores) and LLM application patterns (evaluation, prompt/agent workflows).