AI/ML Engineer III designing and architecting AI solutions at Hewlett Packard Enterprise. Collaborating with teams to drive innovation and tackle complex problems.
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
AI/ML Engineer deploying state - of - the - art AI models to solve real - world problems at Brain Co. Working in healthcare, government, and energy sectors for impactful results.
Trainer at WeAndTheMany facilitating learning by sharing experiences and creating interactive sessions. Engaging with students to enhance their skills and knowledge through dynamic teaching methods.
Machine Learning Manager leading experienced team to drive data - driven AI/ML solutions at Ford. Overseeing strategies for product development focused on analytics in various domains.
Software Engineer I developing machine learning models and applications at Smart Data Solutions. Collaborating to improve infrastructure and automate processes using AI technology.
Intermediate Machine Learning Engineer at Aviva Canada implementing ML pipelines with required collaboration in AI/ML Operations. Join a team dedicated to operationalizing ML models for optimizing solutions.
MLOps Engineer designing and maintaining cloud infrastructure for large - scale computer vision model training. Collaborating with Data Scientists and AI Engineers to streamline model development lifecycle.
Machine Learning Engineer developing AI - first dating solutions at Hinge, enhancing user matchmaking and conversation experience. Collaborating with cross - functional teams to move ML models to production.
Senior ML Engineer designing and developing machine learning models for national security. Collaborating with cross - functional teams to deliver scalable solutions in defense applications.
Machine Learning Engineer developing and deploying ML planning algorithms for autonomous trucks. Join Plus, a leader in AI - based virtual driver software for autonomous trucking.