ML/Data Engineer (Staff/Sr. Consultant level) 8 - 12yrs

Key Skills

Aartificial intelligence, AWS, Big Data Technology, Data Engineering, DevOps, Docker, Large data sets, Machine learning techniques, NoSQL, Python Programming, SPARK Programming, SQL

Job Description

This is a great opportunity to work with a new Data Engineering and MLOps team to scale and structure large scale data engineering and ML/AI that drives significant revenue for Visa. As a member of the Risk and Identify Solutions modeling organization (VPM), your role will involve developing and implementing practices that will allow deployment of machine learning models in large data science projects.

You must be a hands-on expert able to navigate both data engineering and data science disciplines to build effective engineering solutions that support ML/AI models. You will partner closely with global stakeholders in RaIS Product, VPM Data Science and Visa Research to help create and prioritize our strategic roadmap. You will then leverage your expert technical knowledge of data engineering, tools and data architecture in the design and creation of the solutions on our roadmap.

The position is based at Visa's offices in Bangalore, India.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Qualifications

• 8+ yrs. work experience with a bachelor’s degree in an analytical field such as computer science, statistics, finance, economics, or relevant area.

• Working knowledge of Hadoop ecosystem and associated technologies, (For e.g. Apache Spark, Python, Pandas etc.)

Technical skills:

• Strong experience in creating large scale data engineering pipelines, data-based decision-making, and quantitative analysis.
• Experience with SQL for extracting, aggregating, and processing big data Pipelines using Hadoop, EMR & NoSQL Databases.
• Experience with complex, high volume, multi-dimensional data, as well as machine learning models based on unstructured, structured, and streaming datasets.

Preferred skills:

• ETL processes: The role also involves developing and executing large scale ETL processes to support data quality, reporting, data marts, and predictive modeling.
• Spark pipelines: The role requires building and maintaining efficient and robust Spark pipelines to create and access data sets and feature stores for ML models.
• Experience in writing and optimizing spark code and Hive code to process Large Data Sets in Big-Data Environments.
• Strong Development experience in more than one of the following: Golang, Java, Python, Rust.
• Knowledge of standard big data and Real Time stack such as Hadoop, Spark, Kafka, Redis, Flink and similar technologies
• Hands on experience in building and maintaining data pipelines, feature engineering pipelines and comfortable with core ML concepts.
• Hands on experience in engineering, testing, validating and productizing AL/ML models for high performance use cases.
• Exposure to model serving engines such as TensorFlow, Triton etc.
• Exposure to model development frameworks like Ml flow.
• Proficient in managing and operating AWS services including EC2, S3, SageMaker etc.
• Proficient in setting up and managing distributed data and computing environments using AWS services.
• Knowledge about DR / HA topologies, Reliability Engineering with hands on experience in implementing the same.
• Knowledge of using and maintaining DevOps tools and implementing automations for production
• Experience of working with containerized and virtualized environments (Docker, K8s)
• Experience with Unix/Shell or Python scripting and exposure to Scheduling tools like Airflow and Control – M.
• Experience creating/supporting production software/systems and a proven track record of identifying and resolving performance bottlenecks for production systems.
• Exposure to deploying large scale ML/AI models built by the data science teams and experience with development of models is a strong plus.
• Exposure to public cloud equivalents, and ecosystem shall be a plus.
• Strong Experience with Visualization Tools like Tableau, Power BI, is a plus.