Software Engineer GEN AI, Assistant Vice President

Citi is the world's most global bank, serving clients in more than 160 countries. Citi is investing heavily in scaled, enterprise-grade generative and agentic AI that operates securely and reliably across a highly regulated financial infrastructure. You'll join the USCC Architecture and AI Engineering team, which owns an enterprise multi-agent AI platform that autonomously supports the software delivery lifecycle, from product analysis through to reviewed, deployable code.

As a Generative AI Engineer (AVP), you will build and scale production components of this platform. This is a hands-on individual contributor role for an engineer with a strong foundation in computer science, software engineering, and applied AI, ready to ship real production systems rather than one-off prototypes.

Responsibilities

* Build and maintain production AI agents covering product analysis, requirements capture, UI prototyping, backlog generation, and automated code implementation with pull request creation.

* Implement multi-agent workflows using the Google Agent Development Kit (ADK), including sub-agent orchestration, human-in-the-loop approval gates, and agent-to-agent (A2A) messaging.

* Integrate Gemini and Anthropic Claude into agent pipelines via enterprise gateways, applying advanced prompt engineering and structured tool/function calling.

* Connect agents to enterprise systems, including Engineering & AI Product Documentation, JIRA, and GitHub, using MCP-style tool interfaces.

* Build RAG pipelines using vector search, embeddings, and clustering to ground agent responses in accurate enterprise context.

* Build evaluation harnesses (LLM-as-judge, parallel jury methods) to measure faithfulness, relevancy, hallucination rate, and task completion.

* Write advanced, async Python and build well-documented FastAPI services, backed by persistent PostgreSQL session state.

* Apply data structures, algorithms, and complexity analysis to keep orchestration and retrieval logic efficient at scale.

* Instrument services for observability, maintain CI/CD and containerized deployments, apply secure coding and guardrail practices, and write tests (unit, integration, end-to-end) as part of every change.

Core Platform & Application Development

* Full-Stack Engineering & Microservices: Lead the design, implementation, and lifecycle of custom AI platform components. Build production-grade, highly available microservices and full-stack applications using Python, Java (Spring Boot), and modern frontend frameworks (React) for internal LLM/SLM fine-tuning control planes, inference gateways, and experiment tracking dashboards.

* Agentic Frameworks & Patterns: Code, evaluate, and optimize multi-tenant intelligent agents using modular orchestration patterns such as ReAct (Reasoning + Acting) and ReWOO (Reasoning WithOut Observation), leveraging frameworks like Google's Agent Development Kit (ADK), LangGraph, and LangChain.

* Strict Decoupled Integration Layers: Develop and enforce clean, decoupled enterprise integration architectures.

Software Engineering Fundamentals & Distributed Systems

* System Architecture & Design Patterns: Apply clean architecture, domain-driven design (DDD), and SOLID principles across Python and Java codebases. Implement robust design patterns (Strategy, Factory, Adapter, Circuit Breaker, Saga) for modularity and fault isolation.

* Concurrency & High-Throughput Processing: Write robust concurrent and asynchronous code (e.g., Python asyncio, multithreading, Java CompletableFuture/virtual threads, connection pooling) to handle streaming tokens, parallel agent tool execution, and high-concurrency LLM inference loops.

* Reliability, Fault Tolerance & Resiliency: Design systems for high availability, fault tolerance, and graceful degradation, including rate limiting, token bucket algorithms, retry policies with exponential backoff, and distributed caching strategies (e.g., Redis).

* Enterprise API Design: Design clean, versioned, idempotent RESTful APIs, gRPC endpoints, and WebSockets (for real-time streaming LLM outputs) following OpenAPI / Swagger specifications.

Advanced Data Retrieval & Logic Engineering

* Next-Generation RAG Architectures: Build and optimize end-to-end data ingestion and retrieval pipelines for advanced RAG paradigms, including Knowledge Graph RAG (GraphRAG), LightRAG, and hierarchical summary trees (RAPTOR).

* Vector & Graph Integrations: Develop seamless, performant integrations with graph databases (Neo4j) and vector databases (pgvector, Pinecone, Milvus) for complex semantic, hybrid, and relational data retrieval.

* Prompt & Intent Engineering: Design deterministic LLM system instructions, classification logic, and semantic routing trees to prevent intent collisions and hallucinations in complex multi-step workflows.

4 Technical Skills & Core Competencies

A. Software Engineering Fundamentals (Core Pillars)

* Data Structures & Algorithms: Strong command of core data structures (trees, graphs, hash maps, queues, heaps) and algorithmic complexity (Big-O analysis for time and space).

* Object-Oriented & Functional Programming: Good understanding of SOLID principles, clean code, design patterns, separation of concerns, and dependency injection.

* Distributed Systems Concepts: Understanding of CAP theorem, event-driven architectures, asynchronous messaging (Kafka / RabbitMQ), idempotency, distributed transactions, and database isolation levels.

* Testing & Quality Assurance: Comprehensive testing discipline across all layers: Unit testing (pytest, unittest, JUnit, Mockito
* Integration testing, contract testing, and mocking

* Test-Driven Development (TDD) principles

* LLM evaluation and regression testing frameworks

* CI/CD & DevOps Practices: Proficiency with Git workflows, automated CI/CD pipelines, containerization (Docker), container orchestration (Kubernetes / OpenShift), and infrastructure as code.

* Observability & Telemetry: Hands-on experience instrumenting distributed tracing, structured logging, and metrics using OpenTelemetry, Prometheus, Grafana, and ELK/Loki for tracking LLM token latency and tool calls.

* Security & Governance: Understanding of OWASP Top 10, OWASP Top 10 for LLMs, secure API authentication/authorization (OAuth2, OIDC, mTLS, RBAC), secrets management, and data sanitization/PII redaction.

B. AI/ML, Agentic & GenAI Frameworks

* Language & Orchestration: Advanced Python proficiency, prompt engineering, Google ADK, LangChain, LangGraph, and custom state machines.

* Protocol & Standards: In-depth implementation knowledge of Model Context Protocol (MCP) clients and servers.

* RAG & Indexing: GraphRAG, LightRAG, RAPTOR, hybrid search (BM25 + Dense embeddings), re-ranking models (Cross-Encoders), and chunking strategies.

C. Backend, Frontend & Microservices

* Backend: Python (FastAPI, Flask, Django) and Java (Spring Boot, Spring Cloud).

* Frontend: Modern React (hooks, state management, component architecture), TypeScript/JavaScript, and responsive UI design.

D. Data & Storage

* Relational Databases: Oracle, PostgreSQL (schema design, indexing, query optimization, connection management).

* Vector & Graph Stores: Neo4j (Cypher queries), pgvector, Pinecone, Milvus.

* Caching & Message Brokers: Redis, Apache Kafka.

5 Use Case Development & Forward Deployment (FDE)

* SME Collaboration: Act as a Forward Deployed Engineer (FDE), collaborating directly with Subject Matter Experts (SMEs) to translate business and domain logic into automated, production-ready, agent-driven software.

* End-to-End System Integration: Partner with UI engineers and core API platform teams to connect backend agentic logic flawlessly with user-facing React applications and existing enterprise service layers.

* Problem Solving & Communication: Strong analytical skills with the ability to clearly communicate complex technical concepts

Required Qualifications & Skills

* Strong computer science fundament

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