Senior Machine Learning Engineer

ERNI Mandaluyong, National Capital Region, Philippines Overview ERNI is a leading software development company founded in 1994 and headquartered in Switzerland, with over 800 employees worldwide. Our first service center in Asia Pacific, located in Metro Manila (Mandaluyong), supports clients across Europe, APAC, the Philippines, and the USA. We are growing and seeking passionate and motivated individuals to join our team. Role — We are seeking a hands-on MLOps / Machine Learning Engineer with deep expertise in the Azure Databricks ecosystem to help build our AI/ML platform from the ground up. You will work collaboratively with a cross-functional team to implement a predefined architecture and establish the foundations of a production-grade system that adheres to strict data governance and compliance standards. Because this is a greenfield build, the role combines MLOps and Machine Learning engineering responsibilities into a single, integrated position. You will contribute across the end-to-end ML lifecycle from data pipelines and governance through to model deployment and monitoring with a strong focus on collaboration. As the platform grows, you will help shape best practices and define clear processes, ensuring responsibilities remain focused and sustainable. Responsibilities Collaborate with the team to implement the initial AI/ML architecture on Azure Databricks , setting up infrastructure and workflows. Design and build scalable, reproducible ML pipelines for data ingestion, feature engineering, training, testing, and deployment. Ensure version control and collaboration through Git-based workflows. Model Lifecycle & MLOps Establish processes for automated model training, tuning, deployment, and monitoring using Databricks MLflow and Azure ML Pipelines . Define and manage model registries , versioning, and traceability. Data Governance & Compliance Implement strict controls for data access, lineage, and security. Ensure all pipelines and models comply with regulatory requirements, auditability, and ML ethics standards. Embed compliance requirements into system design from day one. Deployment & Monitoring Deploy models for real-time and batch inference using MLflow . Contribute to monitoring frameworks for drift detection, latency, and performance degradation using Azure Monitor or equivalent. CI/CD & Infrastructure Integrate pipelines with Azure DevOps or GitHub Actions for automated testing, validation, and deployment. Support infrastructure-as-code with Terraform , Bicep , or ARM templates to ensure reproducibility and scalability. Partner with data scientists to productionize research prototypes. Document workflows, governance processes, and best practices for ongoing maintainability. Details Seniority level : Not Applicable Employment type : Full-time Job function : Engineering and Information Technology Industries : IT Services and IT Consulting Qualifications 5+ years of experience as an ML Engineer, MLOps Engineer, or similar hybrid role. Strong proficiency in Python and ML frameworks such as Scikit-learn, XGBoost, PyTorch, or TensorFlow. Hands-on experience with Git for pipeline development (branching strategies, code reviews, version control best practices). Solid understanding of MLOps practices, CI/CD, and production ML requirements. Experience working under strict governance and compliance frameworks. Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or related field. Preferred Qualifications Familiarity with Generative AI, LLM deployment, or RAG pipelines using Azure OpenAI Service, LangChain, or open-source LLMs. Experience with vector databases (e.g., FAISS, Milvus, Pinecone) and model explainability tools. Soft Skills Curious and eager to learn — passionate about exploring new technologies and approaches. Collaborative mindset — thrives in a team environment and values collective success. Adaptable — comfortable in a role that spans both ML engineering and MLOps, with evolving responsibilities. Problem-solving skills — able to balance compliance requirements with practical implementation. Attention to detail — especially in governance, testing, and documentation. Proactive communicator — ensures alignment with teammates and stakeholders. #J-18808-Ljbffr