Key Responsibilities

- Design and implement ML / AI pipelines: feature extraction, model training, validation, deployment and monitoring.

- Develop and maintain orchestration workflows (DAGs) using Apache Airflow or Cloud Composer on GCP for model/data workflows.

- Build scalable data pipelines in BigQuery (and other GCP data services) to prepare features and ingestion for ML solutions.

- Work with data engineers, data scientists and software engineers to integrate models into production systems.

- Automate CI/CD for ML code, models and infrastructure (e.g., versioning, rollback, automated tests).

- Monitor model performance (drift detection, data-/model-drift, accuracy/latency metrics), trigger retraining pipelines as needed.

- Optimize cost, performance and scalability of GCP resources used by ML workflows.

- Ensure adherence to governance, security and compliance standards for data and models in production.

- Provide documentation, best-practices, training and support to teams using the MLOps platform.

Required Skills & Experience
- Bachelor s or Master s in Computer Science, Engineering, Data Science or related discipline.

- 3+ years of experience in a cloud environment focusing on ML/AI infrastructure, ideally on GCP.

- Robust hands-on experience with Google Cloud Platform (GCP) services - at least BigQuery, Cloud Storage, Dataflow (nice to have), Cloud Composer/Airflow. (For example,



a previous posting for Incedo indicates BigQuery + Airflow + GCP) hirist.tech+1

- Good programming skills in Python; knowledge of SQL and data manipulation.

- Experience working with orchestration tools (Airflow/Composer) to build and schedule workflows.

- Familiarity with containerisation (Docker), Kubernetes is a plus.

- Experience building, deploying and monitoring ML models in production (model serving, inference pipelines, monitoring).

- Understanding of CI/CD pipelines, infrastructure as code (Terraform, Cloud Formation, or equivalent).

- Strong communication and collaboration skills, ability to work with cross-functional teams.

Desirable / Plus Skills
- Experience with ML frameworks (TensorFlow, PyTorch, Scikit-Learn) and ML lifecycle tools (MLflow, Kubeflow, etc).

- Experience with data engineering frameworks (PySpark, Spark) on GCP or equivalent.

- Experience in building feature stores, realtime streaming ingestion, and event-driven pipelines.

- Domain experience (e.g., finance, telecom, product engineering) as many Incedo clients operate in those sectors.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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