About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.
At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.
Join Visa and do work that matters – to you, to your community, and to the world. Progress starts with you.
Job Description
The Risk and Identity Solutions (RaIS) team provides risk management services for banks, merchants, and other payment networks. The Predictive Fraud Intelligence (PFI) team develops core AI/ML products within the Visa Protect suite, empowering clients to detect and prevent fraud throughout the payment lifecycle. The MLOps and Data Engineering team designs and operates the platforms, pipelines, and tooling that enable the core product teams to build, deploy, and iterate on models quickly. This group provides the scalable data foundations, model‑orchestration frameworks, and automated workflows required to keep fraud‑detection models continuously updated against emerging fraud schemes and new attack vectors.
We’re looking for candidates who are passionate about building high‑performance data systems and who thrive on the challenge of working with petabyte‑scale datasets. If you have experience designing efficient, resilient pipelines optimizing distributed data processing and enabling real‑time insights from massive, complex data flows, we want to meet you. This role is an opportunity to apply deep data‑engineering and MLOps expertise to a mission‑critical domain—empowering fraud‑detection models that protect the entire payment lifecycle.
This is a great opportunity to be part of a Data Engineering and MLOps team that is set out to scale and structure large scale data engineering and ML/AI that drives significant revenue for Visa. As a member of the Predictive Fraud Intelligence – MLOps team based out of Bangalore, your role will involve
- Building and maintaining reliable data pipelines that deliver high‑quality data across the product lifecycle – Product development to client support.
- Developing platforms that support rapid model experimentation, training, evaluation, versioning, and deployment.
- Creating automated monitoring systems for data drift, model performance, and operational health to ensure models stay accurate as fraud patterns evolve.
- Partnering closely with AI/ML researchers and product teams to reduce time from model concept to production.
- Ensuring compliance, security, and traceability across the full ML lifecycle to meet financial‑industry standards.
- Providing self‑service tooling and infrastructure that enables data scientists to iterate quickly while maintaining operational excellence.
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.
The position is based at Visa's offices in Bangalore, India.
What success looks like
- You consistently design and deliver systems that scale to petabytes of data with high reliability, low latency, and efficient resource utilization.
- You provide the architectural direction for the platforms, influencing long‑term technical strategy and raising the engineering bar across the organization.
- You proactively identify gaps in data quality, platform capabilities, and system resilience, and lead cross‑team efforts to close them.
- You mentor engineers across multiple teams, shaping best practices in distributed systems, pipeline design, and machine‑learning operations.
This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications: 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience. Preferred Qualifications: 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience. 8+ yrs. work experience with a bachelor’s degree or 6+ years of work experience with a Master's or Advanced Degree in an analytical field such as computer science, statistics, finance, economics, or relevant area. With relevant experience in handling big data on-premises as well as on cloud. Deep understanding of Hadoop ecosystem and associated technologies and good knowledge of cloud analytical solutions available. Strong expertise in designing and operating large‑scale data pipelines (batch and streaming) that process terabytes to petabytes of data. Deep proficiency with distributed data‑processing frameworks such as Spark, Flink, Beam, or similar. Solid command of data storage technologies (Delta Lake, Iceberg, Hive, BigQuery, Redshift, or equivalent). Working experience with cloud‑based data‑processing systems (AWS EMR, Dataproc, Glue, Dataflow, Snowflake, BigQuery, Redshift, Databricks or equivalent). Strong programming skills in Python, Scala, or Java, with a focus on building reliabe production systems. Hands‑on experience with orchestration and workflow tools (Airflow, Dagster, equivalent). Proficiency in containerization and orchestration (Docker, Kubernetes). Experience implementing CI/CD pipelines for data and ML workloads. Understanding of data‑quality frameworks, lineage, observability, and monitoring (Great Expectations, Deequ, Monte Carlo, Databand, or similar). Practical knowledge of cloud platforms (AWS, GCP, or Azure) and cloud‑native data systems. Demonstrated ability to leverage AI and automation tools in day‑to‑day engineering workflows to increase efficiency and reduce operational overhead. Experience working in fraud detection, risk scoring, payments, or other high‑integrity, compliance‑heavy domains. Familiarity with feature‑store design and operations (Feast, Tecton, or custom implementations). Exposure to real‑time inference architectures and streaming‑based model deployment. Experience with modern MLOps platforms and tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent). Experience optimizing cost efficiency at scale (storage formats, compute tuning, autoscaling, caching strategies). Ability to influence architecture across multiple teams and drive long‑term platform strategy. Strong communication skills for partnering with data scientists, product leaders, and engineering leadership. Experience mentoring senior engineers and shaping engineering culture. Understanding of and interest in Generative AI, large language models, and how they apply to data engineering, MLOps, and developer productivity. Strong experience in end-to-end analytics on any public cloud (preferably AWS)
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.