Develop advanced ML/AI models leveraging both structured and unstructured datasets for batch and online inferencing use cases within the Customer Support and Employee Experience domains..
Leverage or build analytics tools that utilize the data pipeline to provide significant insights into customer case data, bug data, operational, and other key business performance metrics..
Collaborate with partners including the executive, product, data, and operations teams to transform business priorities into ML/AI problems and develop solutions..
Work with MLOps specialists to manage the full life cycle of model development from concept to production..
Collaborate with data and analytics specialists to strive for greater functionality in our data systems..
Identify trends and patterns from datasets to scope opportunities for automation..
Qualifications And Desired Experiences.
7+ years of experience in an AI Engineer role, with a Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field..
6+ years of experience in end-to-end architecting of advanced ML and AI solutions..
Strong hands-on coding skills (preferably in Python) for processing large-scale data sets and developing machine learning models leveraging both structured and unstructured data..
Experience working in a technical support environment, handling datasets from CRM, bug systems, and logs..
Experience supporting and working with multi-functional teams in a multidimensional environment..
Good team player with excellent interpersonal, written, verbal, and presentation skills..
Create and maintain optimal data pipeline architecture, assembling large, sophisticated data sets that meet functional/non-functional business requirements..
Experience working with Large Language Models, Generative AI, and Conversational AI..
Familiarity with one or more machine learning or statistical modeling tools such as Numpy, ScikitLearn, MLlib, Tensorflow, and NLP libraries..
Experience working with Databricks and Snowflake platforms..
Experience with AWS, S3, Spark, Kafka, and Elastic Search..
Experience with AWS cloud services: EC2, EMR, RDS, and Redshift..
Experience with stream-processing systems: Storm, Spark-Streaming, etc..
Experience with big data tools: Hadoop, Spark, Kafka, etc..
Experience with relational SQL and NoSQL databases, including Postgres and Cassandra..
Familiarity with building and optimizing data pipelines, architectures, and data sets..
Familiarity with MLOps practices and Agile development framework..
Familiarity with CRM platforms such as Salesforce..
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and find opportunities for improvement.
