Software Engineer - Sr. Consultant Level - Python

We are seeking a GenAI Architect / Sr Consultant, Software Engineer (L6.5) who is both a hands-on full-stack engineer and a team leader capable of designing, developing, and deploying GenAI-powered applications. You will manage and mentor an engineering team while delivering scalable AI systems that integrate with Visas product ecosystem, developer platforms, and internal operational tools.

This role blends architecture, leadership, full-stack development, cloud engineering, and GenAI frameworks. As a Visa Lead Engineer, you will be an integral part of a multi-functional development team inventing, designing, building, and testing software products that reach a truly global customer base. While building components of powerful payment technology, you will get to see your efforts shaping the digital future of monetary transactions.

The Work itself:

• Global Operations holds the responsibility of ensuring Visas diverse portfolioof commercial services are performing optimally, meeting stringent availability targets and exceeding our clients expectations.
• By architecting automation and GenAI into our operational DNA, and deeply embedding AIOps into our strategic foundation, Global Operations, the engine room of production operations, is well positioned to think big, act quickly and innovate with intention.
• Working with highly integrated, high-throughput systems, an equally high importance must be placed on robust observability,intelligent event detection and incident management.
• Global Operations sets the operational standards to support the business demands of today and the operational needs of tomorrow as technologies continue to evolve and adjust the way we work to support the business.
• Through blame free post mortems, intellectual curiosity and problem solving we turn reactive into proactive and create an environment that provides the support and mentorship for our team members to learn and grow.

Qualifications

Basic Qualifications:

• 8+ years of relevant work experience with a Bachelors Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11+ years of relevant work experience.
• Preferred Qualifications:
• 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
• Bachelors or Masters in Computer Science, Engineering, AI/ML, or related field.
• 8+ years of software engineering experience; 2+ years leading or mentoring engineers.
• Worked with Agile SDLC process and SAFE agile environment in distributed teams
• Hands-on experience with:
• LLM frameworks (LangGraph, LangChain, Haystack, AWS Bedrock GenAI tools)
• Python backend development (FastAPI strongly preferred)
• Frontend development (React + TypeScript)
• Vector databases (FAISS, Pinecone, Weaviate, OpenSearch, etc.)
• Agile Frameworks like SAFE, GitHub / Bitbucket, Jira, ServiceNow, Wiki, CI/CD automation pipelines involving Jenkins
• Proven experience shipping products on AWS (Lambda, ECS/EKS, S3, API Gateway, Bedrock, SageMaker).
• Strong understanding of distributed systems, microservices, and API design.
• Experience implementing RAG at scale using internal or cloud-based vector stores.
• Experience with Databricks/Spark pipelines, MLFlow, or modern MLOps tools.
• Familiarity with event-driven messaging systems (Kafka, Kinesis).
• Background in payments, risk, fraud analytics, or regulated financial systems.
• Experience creating developer tooling or reusable frameworks for broader engineering teams.

The Skills You Bring:

• GenAI Architecture & Framework Development
• Architect and deliver GenAI-enabled applications leveraging:
• LangGraph, LangChain, Haystack, or similar agent frameworks
• AWS GenAI services (Bedrock, SageMaker, Strands-style agent pipelines, embeddings, vector stores)
• RAG architectures, custom LLM orchestration, model evaluation, LLMOps workflows
• Build reusable GenAI components, SDKs, pipelines, and abstraction layers for engineering teams.
• Define best practices for:
• Prompt engineering and evaluation
• LLM safety, guardrails, and content filtering
• Embedding generation and vector database integrations
• Model lifecycle governance and deployment optimization
• Full-Stack Engineering (Hands-on Development)
• Develop end-to-end AI applications using:
• Backend: Pyt…