The Senior Consultant - AI & Data Engineer is a highly skilled technical expert responsible for building enterprise‑grade AI/ML solutions, GenAI applications, and scalable data engineering pipelines that enhance operational excellence across Visa’s Client Services organization. This role is fully hands‑on and focuses on advanced engineering, solution delivery, and end‑to‑end implementation of AI‑driven data products.

The ideal candidate brings strong practical experience in AI engineering (3–4+ years) combined with deep expertise in data engineering, distributed systems, and modern MLOps practices. This role does not require people management but demands strong individual technical leadership, problem‑solving capability, and excellence in architecture, coding, and solution delivery.

Key Responsibilities

AI Engineering & GenAI Solutions:

• Design, develop, and deploy LLM‑based applications, retrieval‑augmented generation (RAG) pipelines, embeddings‑powered search, and NLP/ML models.
• Build GenAI‑driven data products such as document intelligence systems, knowledge assistants, summarization services, conversational interfaces, and automated reasoning tools.
• Implement advanced AI engineering techniques including model fine‑tuning, prompt optimization, vector indexing, and scalable inference.
• Ensure AI systems comply with Visa’s standards for security, reliability, performance, monitoring, and responsible AI governance.

Data Engineering & Pipeline Development:

• Build and optimize scalable data pipelines, ETL/ELT workflows, and distributed processing systems to support AI/ML and analytics use cases.
• Develop high‑quality data models, transformations, and feature pipelines for production ML systems.
• Integrate structured and unstructured data sources from multiple internal and external systems into enterprise data platforms.
• Implement data quality, lineage, observability, audit logging, and metadata management for mission‑critical datasets.

MLOps & Platform Engineering:

• Develop end‑to‑end ML pipelines including model training, evaluation, deployment, and monitoring using modern MLOps frameworks.
• Implement CI/CD workflows, automated testing, feature stores, experiment tracking, and scalable serving layers.
• Build containerized, cloud‑native AI services using Docker, Kubernetes, and serverless components.
• Optimize performance across GPU workloads, vector retrieval systems, and large‑scale batch/streaming jobs.

Technical Collaboration & Solution Delivery:

• Work closely with product owners, architects, data scientists, and engineering teams to translate business requirements into robust technical designs.
• Participate in architecture reviews, propose design improvements, and guide best practices in coding, data modeling, and ML lifecycle management.
• Deliver well‑documented, maintainable, and secure production code.
• Troubleshoot complex AI, pipeline, and data performance issues in development, staging, and production environments.

This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.

Required Qualifications:
8–9+ years of experience in data engineering, AI engineering, or machine learning development roles.
3–4+ years of strong hands‑on experience building and deploying production AI/ML solutions.

Expertise in:
Python (including ML libraries), SQL, distributed systems
AI/ML frameworks: PyTorch, TensorFlow, Scikit-learn, and GenAI toolkits
Vector DBs and RAG stack: FAISS, Milvus, Elasticsearch
Data engineering tools: Spark, Kafka, Airflow, Databricks, or similar
Cloud platforms: AWS, GCP, or Azure
Containerization and orchestration: Docker, Kubernetes

Proven ability to build:
GenAI data products (LLM apps, knowledge retrieval, chat interfaces)
Data pipelines powering ML and analytics
Scalable, secure inference services
Strong knowledge of data modeling, distributed computing, and modern data architectures.
Skilled in CI/CD, testing, logging, monitoring, and engineering best practices for ML systems.
Ability to collaborate with cross‑functional teams, gather requirements, and deliver technical solutions independently.

Preferred Qualifications:
Experience with LLM fine‑tuning, embeddings optimization, or domain adaptation techniques.
Familiarity with AI governance, responsible AI, and enterprise security/compliance standards.
Experience with feature stores, experiment tracking, and advanced MLOps frameworks.
Background in financial services, payments, or client operations environments.
Master’s degree in Computer Science, Engineering, or related technical discipline.

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.