Machine Learning Engineer Full-time

Responsibilities

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.

Additional Responsibilities:

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.

Technical and Professional Requirements:

Bachelor’s or Master’s 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.

Preferred Skills:

Technology->Machine learning->data science