Machine Learning Engineer

Machine Learning Engineer:

The Machine Learning Engineer will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.

You will be part of a collaborative interdisciplinary team around data, where you will be responsible of our continuous delivery of statistical/ML models. You will work closely with process owners, product owners and final business users. This will provide you the correct visibility and understanding of criticality of your developments.

• Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
• Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
• Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
• Use big data technologies to help process data and build scaled data pipelines (batch to real time)
• Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines
• Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
• Automate ML models deployments
• Work timing-11:30 AM-9:00 PM

• 8 years of overall experience that includes at least 4+ years of hands-on work experience data science / Machine learning
• Minimum 4+ year of SQL experience
• Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred

• BE/BS in Computer Science, Math, Physics, or other technical fields.

Skills, Abilities, Knowledge:

• Data Science Hands on experience and strong knowledge implementing & productionizing machine learning models supervised and unsupervised models. Deployment of models in am MLOps framework is required. Knowledge of Demand Forecast models is a plus.
• Programming Skills Hands-on experience in statistical programming languages like Python, R and database query languages like SQL
• Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators is a plus
• Cloud (Azure) Experience in Databricks and ADF is required
• Familiarity with Spark, Hive, Pigis an added advantage
• Model deployment experience will be a plus
• Experience with version control systems like GitHub and CI/CD tools