Machine Learning Engineer

About the Role

Phonologies is seeking a hands-on ML Engineer who bridges data engineering and machine learning designing and implementing production-ready ML pipelines that are reliable, scalable, and automation-driven.

You'll own the end-to-end workflow: from data transformation and pipeline design to model packaging, fine-tuning preparation, and deployment automation.

This is not a Data Scientist or MLOps role its data-focused engineering position for someone who understands ML systems deeply, builds robust data workflows, and develops the platforms that power AI in production.

Role & responsibilities

Machine Learning Pipelines & Automation:

- Design, deploy and maintain end-to-end ML pipelines. - Build data transformation tiers (Bronze, Silver, Gold) to enable structured, reusable workflows. - Automate retraining, validation, and deployment using Airflow, Kubeflow, or equivalent tools. - Implement Rules-Based Access Control (RBAC) and enterprise-grade authorization.

API & Platform Architecture:

- Develop robust APIs for model serving, metadata access, and pipeline orchestration. - Participate in platform design and architecture reviews, contributing to scalable ML system blueprints. - Create monitoring and observability frameworks for performance and reliability.

Cloud & Deployment:

- Deploy pipelines across cloud (AWS, Azure, GCP) and on-prem environments, ensuring scalability and reproducibility. - Collaborate with DevOps and platform teams to optimize compute, storage, and workflow orchestration.

Collaboration & Integration:

- Work with Data Scientists to productionize models, and with Data Engineers to optimize feature pipelines. - Integrate Firebase workflows or data event triggers where required.

Preferred candidate profile

Experience: 5+ years in Data Engineering or ML Engineering, with proven experience in:

- building data workflows and ML pipelines - packaging and deploying models using Docker & CI/CD - designing platform and API architecture for ML systems.

Technical Skills:

- Programming & ML: Python, SQL, scikit-learn, XGBoost, LightGBM - Data Engineering & Cloud Pipelines: Large-scale preprocessing, containerized ETL (Docker, Airflow, Kubernetes), workflow automation - Data Streaming & Integration: Apache Kafka, micro-batch and real-time ingestion - ML Lifecycle & Orchestration: MLFlow, CI/CD, Dagshub, Databricks, A/B Testing, modular ML system design - API & Platform Development: FastAPI, Flask, RESTful APIs, architecture planning - Data Governance, Privacy, Security & Access Control: Schema registry, lineage tracking, secure data handling, audit logging, RBAC - AutoML & Optimization: PyCaret, H2O.ai, Google AutoML - Model Monitoring & Automation: Drift detection, retraining workflows, Airflow / Kubeflow automation

Education: Bachelors or Masters in Computer Science, Machine Learning, or Information Systems.

Communication & Collaboration: Translating technical concepts, business storytelling, cross-functional delivery.