Senior Machine Learning Engineer

This position offers you the opportunity to join a fast-growing technology organization that is redefining productivity paradigms in the software engineering industry. Thanks to our flexible, distributed model of global operation and the high caliber of our experts, we have enjoyed triple digit growth over the past five years, creating amazing career opportunities for our people. If you want to accelerate your career working with like-minded subject matter experts, solving interesting problems and building the products of tomorrow, this opportunity is for you. Purpose Design, implement, and deploy Machine Learning models that power data-driven decision-making and document processing. This role will focus on building reliable and scalable models while ensuring data quality and integration across distributed teams. Key Responsibilities Develop and maintain Machine Learning models that enhance business processes, automate insights, and support predictive decision-making. Design and implement feature engineering, model evaluation, and performance monitoring pipelines. Translate business requirements into production-ready ML solutions, optimizing for scalability and maintainability within a Microsoft-based environment. Integrate models with applications and APIs (primarily in .NET environments) to support automation and analytics use cases. Develop robust data ingestion, cleaning, and preparation strategies to support independent model development. Contribute to the definition and implementation of MLOps practices for model deployment, tracking, and retraining. Work with AI and Engineering teams to ensure alignment between data, model design, and business goals. Core Skills & Experience 1. Machine Learning & Data Science Strong experience in developing, validating, and deploying ML models (e.g., regression, classification, clustering, or recommendation systems). Understanding of statistical modeling, feature selection, and performance tuning. Experience working with structured and unstructured data, including text-based or document datasets. Familiarity with Natural Language Processing (NLP) techniques is a plus (e.g., entity extraction, summarization, embeddings). 2. Data & Analytics Experience managing data cleaning, preparation, and quality validation processes. Understanding of ETL workflows and the ability to collaborate effectively with remote or distributed data engineering teams. Familiarity with Azure Data Factory, Azure Synapse, or similar data orchestration tools. Proficiency in SQL for data exploration, validation, and aggregation. 3. Model Deployment & Operations Knowledge of Azure Machine Learning, Azure DevOps, or equivalent model deployment and monitoring tools. Understanding of MLOps concepts such as versioning, experiment tracking, and model lifecycle automation. Experience with containerization (Docker) and API-based integration for serving ML models in production environments. 4. Programming & Integration Proficiency in a general-purpose programming language for ML implementation — Python preferred, but .NET (C#) or other languages with ML libraries are acceptable. Experience integrating ML components into microservices or enterprise systems. Familiarity with REST APIs, event-driven architectures, and data serialization formats (JSON, Parquet, etc.). Preferred Qualifications Experience deploying ML models in a Microsoft Azure environment. Knowledge of Azure Machine Learning, Azure Cognitive Services, or Azure Databricks. Exposure to Generative AI or LLMs for business document processing or decision support. Microsoft Certified: Azure Data Scientist Associate (DP-100) or related certification. Soft Skills Strong analytical and problem-solving mindset. Excellent collaboration skills, particularly with cross-functional and distributed teams. Ability to communicate technical concepts clearly to non-technical stakeholders. Proactive, self-organized, and capable of managing priorities in a dynamic environment. #J-18808-Ljbffr