About Motadata
Motadata is a renowned IT monitoring and management software company that has been transforming how businesses manage their ITOps since its inception.
Our vision is to revolutionize the way organizations extract valuable insights from their IT networks.Bootstrapped since inception, Motadata has built up a formidable product suite comprising cutting-edge solutions, empowering enterprises to make informed decisions and optimize their IT infrastructure.As a market leader, we take pride in our ability to collect and analyze data from various sources, in any format, providing a unified view of IT monitoring data.
Position Overview
We are seeking a Senior Machine Learning Engineer to join our team, focused on enhancing our AIOps and IT Service Management (ITSM) product through the integration of cutting-edge AI/ML features and functionality.
As part of our innovative approach to revolutionizing the IT industry, you will play a pivotal role in leveraging data analysis techniques and advanced machine learning algorithms to drive meaningful insights and optimize our product's performance.With a particular emphasis on end-to-end machine learning lifecycle management and MLOps, you will collaborate with cross-functional teams to develop, deploy, and continuously improve AI-driven solutions tailored to our customers' needs.From semantic search and AI chatbots to root cause analysis based on metrics, logs, and traces, you will have the opportunity to tackle diverse challenges and shape the future of intelligent IT operations.
Role & Responsibility
- Lead the end-to-end machine learning lifecycle, understand the business problem statement, convert into ML problem statement, data acquisition, exploration, feature engineering, model selection, training, evaluation, deployment, and monitoring (MLOps).
- Should be able to lead the team of ML Engineers to solve the business problem and get it implemented in the product, QA validated and improvise based on the feedback from the customer.
- Collaborate with product managers to understand business needs and translate them into technical requirements for AI/ML solutions.
- Design, develop, and implement machine learning algorithms and models, including but not limited to statistics, regression, classification, clustering, and transformer-based architectures.
- Preprocess and analyze large datasets to extract meaningful insights and prepare data for model training.
- Build and optimize machine learning pipelines for model training and inference using relevant frameworks.
- Fine-tune existing models and/or train custom models to address specific use cases.
- Enhance the accuracy and performance of existing AI/ML models through monitoring, iterative refinement and optimization techniques.
- Collaborate closely with cross-functional teams to integrate AI/ML features seamlessly into our product, ensuring scalability, reliability, and maintainability.
- Document your work clearly and concisely for future reference and knowledge sharing within the team.
- Stay ahead of latest developments in machine learning research and technology and evaluate their potential applicability to our product roadmap.
Skills And Qualifications
- Bachelor's or higher degree in Computer Science, Engineering, Mathematics, or related field.
- Minimum 5+ years of experience as a Machine Learning Engineer or similar role.
- Proficiency in data analysis techniques and tools to derive actionable insights from complex datasets.
- Solid understanding and practical experience with machine learning algorithms and techniques, including statistics, regression, classification, clustering, and transformer-based models.
- Hands-on experience with end-to-end machine learning lifecycle management and MLOps practices.
- Proficiency in programming languages such as Python and familiarity with at least one of the following : Java,Golang, .NET, Rust.
- Experience with machine learning frameworks/libraries (e.g. , TensorFlow, PyTorch, scikit-learn) and MLOps tools (e.g. , MLflow, Kubeflow).
- Experience with ML.NET and other machine learning frameworks.
- Familiarity with natural language processing (NLP) techniques and tools.
- Excellent communication and teamwork skills, with the ability to effectively convey complex technical concepts to diverse audiences.
- Proven track record of delivering high-quality, scalable machine learning solutions in a production environment.
(ref:hirist.tech)
