Machine Learning Engineer

Responsibilities: As part of this role, you ll need to comprehend various ML algorithms, their strengths, weaknesses, and how they impact deployment. Algorithm Development: - Design, develop, and implement machine learning algorithms to address specific business challenges. - Collaborate with cross-functional teams to understand requirements and deliver solutions that meet business objectives. Data Analysis and Modeling: - Perform exploratory data analysis to gain insights and identify patterns in large datasets. - Build, validate, and deploy machine learning models for predictive and prescriptive analytics. Feature Engineering: - Extract and engineer relevant features from diverse datasets to enhance model performance. - Optimize and fine-tune models for improved accuracy and efficiency. Model Evaluation and Deployment: - Conduct thorough evaluation of machine learning models using appropriate metrics. - Deploy models into production environments, ensuring scalability, reliability, and performance. - Communicate complex technical concepts to non-technical stakeholders effectively. Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, or a related field. 5-6 years of hands-on experience in developing and deploying machine learning models. Proficiency in programming languages such as Python, R, or Java. Experience with data preprocessing, feature engineering, and model evaluation techniques. Understanding how to set up scalable and reliable environments for ML models is crucial. Mastery of CI/CD and Automation Tools: Continuous Integration/Continuous Deployment Knowledge of tools like Azure ML DevOps, Jenkins, GitLab CI/CD, and Kubernetes to automate workflows and ensure smooth deployments. Knowledge of Monitoring and Logging Systems: Azure Monitor, Prometheus, Grafana, and ELK stack for monitoring and logging. Strong Communication and Collaboration Abilities: As a team lead, the candidate will work closely with data scientists, engineers, and stakeholders. Understanding of forecasting & revenue ERP environments (e.g.: Salesforce & SAP ECC) Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn). Deep Understanding of Machine Learning Models Proficiency in Cloud and On-Premises Infrastructure Excellent communication skills for aligning goals, resolving conflicts, and driving successful ML projects. Continuous Learning: Stay abreast of the latest developments in machine learning, data science, and related fields.