Machine Learning Engineer

As a hands-on ML Engineer at Phonologies, your role involves bridging data engineering and machine learning to design and implement production-ready ML pipelines that are reliable, scalable, and automation-driven. You will be responsible for owning the end-to-end workflow, from data transformation and pipeline design to model packaging, fine-tuning preparation, and deployment automation. This is a data-focused engineering position that requires a deep understanding of ML systems, building robust data workflows, and developing platforms that power AI in production. **Key Responsibilities:** - 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. - 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. - 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. - Work with Data Scientists to productionize models, and with Data Engineers to optimize feature pipelines. - Integrate Firebase workflows or data event triggers where required. **Qualifications Required:** - Experience of 5+ years in Data Engineering or ML Engineering. - Proven experience in building data workflows and ML pipelines, packaging and deploying models using Docker & CI/CD, and designing platform and API architecture for ML systems. **Additional Details (if present in the original JD):** Phonologies is looking for candidates with a Bachelors or Masters in Computer Science, Machine Learning, or Information Systems. Strong technical skills in Python, SQL, scikit-learn, XGBoost, LightGBM, along with expertise in data engineering, cloud pipelines, data streaming, ML lifecycle, API & platform development, data governance, privacy, security, access control, AutoML, and model monitoring & automation are highly preferred. Effective communication and collaboration skills, including translating technical concepts and business storytelling, are key for this role.,