Machine Learning Engineer at SYNCPILOT developing AI functionalities in a Microservice architecture. Collaborating with experts and integrating ML/AI into existing systems.
About the role
- Senior Machine Learning Engineer focusing on developing scalable ML models for data-driven decision-making. Join a fast-growing tech company redefining productivity paradigms.
Responsibilities
- Design, implement, and deploy Machine Learning models that power data-driven decision-making and document processing
- Focus on building reliable and scalable models while ensuring data quality and integration across distributed teams
- 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
Requirements
- 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)
- 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
- 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
- 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.)
- 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
- 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.
Benefits
- Flexible work arrangements
- Professional development
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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