AI Engineer - Machine Learning

What You’ll Do:

• 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.

What You’ll Bring

• A master's or bachelor’s degree in Computer Science or related field from a top university.
• 1-3 years’ hands-on experience in ML development.
• Good understanding of the fundamentals of machine learning
• Strong programming expertise in Python, PySpark/Scala. 
• 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 working with MLFlow
• Experience with Spark or other distributed computing frameworks.
• Experience in ML platforms like Sage maker, Kubeflow.
• Experience with pipeline orchestration tools such Airflow.
• Experience in deploying models to cloud services like AWS, Azure, GCP, Azure ML.
• Expertise in SQL, SQL DB's.
• Knowledgeable of core CS concepts such as common data structures and algorithms.
• Collaborate well with teams with different backgrounds / expertise / functions
• Fluency in English
• Client-first mentality
• Intense work ethic
• Collabrative spirit and probelm-solving approach 

How you’ll grow:

• Cross-functional skills development & custom learning pathways
• Milestone training programs aligned to career progression opportunities
  • Internal mobility paths that empower growth via s-curves, individual contribution and role expansions