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.