Architect and evolve the Agentic AI Platform to support multi-agent orchestration, retrieval-augmented generation (RAG), and integration with existing Visa microservices.
Design scalable, secure backend systems using Java (Spring Boot) and Python (FastAPI/Flask).
Develop RESTful and gRPC APIs that connect backend services, AI agents, and React front-end applications.
AI & GenAI Integration
Integrate Large Language Models (LLMs) such as GPT, Claude, Mistral, and Gemini into backend systems.
Build and optimize RAG pipelines using vector databases (Pinecone, Weaviate, FAISS).
Develop orchestration and agentic workflows using LangChain, LangGraph, or Autogen.
Scalability & Reliability
Lead the design and implementation of distributed, fault-tolerant systems that scale horizontally.
Implement auto-scaling, monitoring, and observability using Kubernetes, Docker, and Prometheus/Grafana.
Drive best practices for latency reduction, cost optimization, and service reliability.
Leadership & Execution
Partner with other senior engineers, ML engineers, and product leads to define and deliver the platform roadmap.
Mentor developers, set coding standards, and lead design reviews.
Deliver results with urgency — balancing innovation with enterprise rigor.
Innovation & Strategy
Explore new frameworks, architectures, and deployment models to push the boundaries of Agentic AI in production.
Drive continuous improvement, automation, and open-source adoption across the engineering organization.
AI Security & Governance
Design secure, compliant Agentic AI systems with built‑in safeguards for data privacy, prompt security, auditability, and responsible AI usage across enterprise environments.
LLM Quality & Evaluation
Define evaluation metrics and testing frameworks to measure LLM and agent performance, accuracy, latency, cost, and reliability in production.
Platform & Developer Enablement
Build reusable AI platform components, SDKs, and internal tooling that accelerate adoption and enable teams to safely develop, deploy, and scale Agentic AI solutions.
Observability & Explainability
Implement end‑to‑end observability for AI workflows, including tracing, logging, and monitoring of agent decisions and tool usage across distributed systems.
Operational Excellence
Lead production readiness, incident response, and post‑mortems for AI services, driving continuous improvements in resilience, scalability, and deployment practices.
Cross‑Functional Technical Leadership
Collaborate with product, data, ML, and security teams to translate business needs into scalable AI architectures and influence platform strategy and roadmap.
- This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.
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Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
11+ years of backend software development experience with Java and Spring Boot.
2+ years of backend experience with Python (FastAPI, Flask, or similar frameworks).
Production-level experience in building and deploying AI Agentic solutions, including orchestration of LLM-based agents, integration with enterprise microservices, and troubleshooting live production environments.
Hands-on experience implementing LLM, GenAI, and RAG systems using frameworks such as LangChain, LangGraph, or Autogen.
Experience building and integrating Model Context Protocol (MCP) frameworks for tool and API interoperability
Strong understanding of AI orchestration and LLM integration patterns in distributed environments.
Proven experience building and scaling distributed microservice architectures.
Proficiency in REST/gRPC API design, event-driven architectures, and message queues (Kafka, RabbitMQ).
Experience with vector databases (Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) pipelines.
Hands-on expertise with cloud platforms (AWS, GCP, Azure).
Familiarity with SQL and NoSQL databases (MySQL, DynamoDB, MongoDB).
Experience with containerization and CI/CD pipelines (Docker, Kubernetes, ArgoCD, Jenkins, GitHub Actions).
Excellent debugging, performance optimization, and system design skills at scale.
Leadership Attributes
Go-Getter: Executes decisively, drives clarity, and thrives in ambiguity.
Builder: Hands-on technologist who delivers high-impact systems from prototype to production.
Hustler: Operates with urgency and persistence to overcome obstacles.
Entrepreneurial: Innovates beyond defined scope, with ownership and bias for action.
True North: Leads with integrity, clarity, and long-term vision.
Lead by Example: Demonstrates accountability, openness, and excellence in technical and people leadership.
Execute with Excellence: Sets a high bar for quality, collaboration, and delivery across multi-disciplinary engineering teams.
Tech Stack Snapshot
Languages: Java (Spring Boot), Python (FastAPI, Flask), TypeScript
AI Frameworks: LangChain, LangGraph, Autogen, LlamaIndex
LLMs: GPT, Claude, Mistral, Gemini
Data: MySQL, Hazelcast, Pinecone, FAISS
Infra: Docker, Kubernetes, ArgoCD, AWS/GCP
Frontend Integration: React, GraphQL, REST/gRPC APIs
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.
