A Machine Learning Engineer will be responsible for leading teams in client projects to deliver many of the activities below:

Responsibilities

• Build, Refine and Use ML Engineering platforms and components
• Scaling machine learning algorithms to work on massive data sets and strict SLAs
• Build and orchestrate model pipelines including feature engineering, inferencing and continuous model training
• Implement ML Ops including model KPI measurements, tracking, model drift & model feedback loop
• Collaborate with client facing teams to understand business context at a high level and contribute in technical requirement gathering;
• Implement basic features aligning with technical requirements;
• Write production-ready code that is easily testable, understood by other developers and accounts for edge cases and errors;
• Ensure highest quality of deliverables by following architecture/design guidelines, coding best practices, periodic design/code reviews;
• Write unit tests as well as higher level tests to handle expected edge cases and errors gracefully, as well as happy paths;
• Uses bug tracking, code review, version control and other tools to organize and deliver work;
• Participate in scrum calls and agile ceremonies, and effectively communicate work progress, issues and dependencies;
• Consistently contribute in researching & evaluating latest architecture patterns/technologies through rapid learning, conducting proof-of-concepts and creating prototype solutions.

Qualification & Experience

• Minimum 3 years’ experience in deploying and productionizing ML models
• Bachelor’s degree
• Expertise in crafting ML Models for high performance and scalability
• Experience in implementing feature engineering, inferencing pipelines and real time model predictions
• Experience in ML Ops to measure and track model performance
• Experience with Spark or other distributed computing frameworks
• Strong programming expertise in PySpark , Python, Scala or Java
• Experience in ML platforms like Sagemaker, MLFlow, Kubeflow or other platforms
• Experience in deploying models to cloud services like AWS, Azure, GCP
• Good fundamentals of machine learning and deep learning
• Knowledgeable of core CS concepts such as common data structures and algorithms
• Collaborate well with teams with different backgrounds / expertise / functions.

Additional Skills

• Understanding of DevOps, CI / CD, data security, experience in designing on cloud platform;
• Experience in data engineering in Big Data systems
• Willingness to travel to other global offices as needed to work with client or other internal project teams.