VP, Applications Development – Tech Lead (Java, UI, PL-SQL & Agentic AI)

Citi; Tampa (FL); On-site; USD 180,000 - 240,000; Full time

Job summary

Citi is hiring an experienced Senior Generative AI Developer to design and integrate agentic AI across our Controls Technology platform. You will craft robust agent systems atop foundation models, focusing on context engineering, RAG, and multi-agent orchestration.

Collaboration with cross-functional teams is essential to deliver scalable, compliant AI solutions that enhance automation. You will mentor juniors, review code, and help shape best-practice governance, safety, and observability for

Qualifications

• Deep, hands-on expertise in foundation models, LLMs, embeddings, tokenization, and context-window management.
• Advanced skills in prompt and context engineering, including tools/frameworks and dynamic context orchestration.
• Strong experience building RAG systems with chunking, hybrid search, and multi-vector retrieval.
• Experience designing knowledge graphs and Graph RAG pipelines (Neo4j, ArangoDB) for multi-hop retrieval.
• Proven track record building agentic AI systems with Google ADK and related frameworks, including tool calling and memory.
• Solid grasp of multi-agent orchestration patterns and harness engineering for governance and safety.
• Hands-on with MCP and A2A protocols for tool/data access and inter-agent collaboration.
• Experience with agent observability (OpenTelemetry) for production systems.
• Proficiency with major GenAI APIs (OpenAI, Gemini, Claude) and orchestration tools (LangChain, LlamaIndex).
• Strong NLP skills (NER, parsing, classification, topic modeling).
• Proficiency with vector databases and embedding models for large-scale retrieval.
• Experience with Docker, Kubernetes, and CI/CD for AI/agentic apps.
• Solid understanding of AI compliance, guardrails, and responsible AI practices.
• Strong Python skills and data preprocessing, document ingestion, and API development.

Responsibilities

• Collaborate with AI architects to design and implement generative and agentic AI solutions for enterprise challenges.
• Architect advanced context engineering strategies to maximize reliability, provenance, and token efficiency.
• Design and implement advanced generative AI methods, including prompt engineering and RAG.
• Build and optimize RAG systems with hybrid search and multi-vector retrieval.
• Design knowledge graphs and Graph RAG architectures for grounded, multi-hop reasoning.
• Architect agentic workflows and multi-agent systems using ADK and comparable frameworks, with orchestration patterns.
• Develop robust agent harnesses with governance, constraints, and execution controls.
• Integrate agents with tools via MCP and coordinate inter-agent collaboration via A2A.
• Support production deployment, scalability, observability, and maintainability of GenAI/agentic apps.
• Contribute to real-time and streaming AI solution development and optimization.
• Stay current with advances in generative/agent AI and share knowledge with the team.
• Ensure adherence to ethical AI guardrails, data privacy, and compliance standards.
• Mentor junior engineers, provide code reviews, and promote technical excellence.

Skills

Foundation models

LLMs

Embeddings

Tokenization

Context window

Prompt engineering

Context engineering

Prompt design tools

Dynamic context orchestration

RAG systems

Chunking strategies

Hybrid search

Multi-vector retrieval

Knowledge graphs

Graph RAG

Neo4j

ArangoDB

Google ADK

LangGraph

Agent Framework

CrewAI

OpenAI Agents SDK

Supervisor/Worker

Hierarchical

Peer-to-peer

MCP

A2A

OpenTelemetry

OpenAI APIs

Gemini

Claude

LangChain

LlamaIndex

NER

Dependency parsing

Text classification

Topic modeling

Vector databases

Embedding models

Docker

Kubernetes

CI/CD

AI compliance

Guardrails

Python

Data preprocessing

Document ingestion

API development

Tools

Neo4j

ArangoDB

Google ADK

LangGraph

CrewAI

OpenAI Agents SDK

LangChain

LlamaIndex

Docker

Kubernetes

CI/CD

Job description

We are seeking an experienced Senior Generative AI Developer to help drive the design, development, and integration of state-of-the-art Generative AI and agentic AI solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models - not on training or fine-tuning models.

Key Responsibilities

• Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges.
• Architect advanced context engineering strategies - context layering, chaining, compression, pruning/offloading, and memory management - to maximize reliability, provenance, and token efficiency in production.
• Design and implement advanced generative AI methods, including sophisticated prompt engineering and Retrieval-Augmented Generation (RAG).
• Build and optimize RAG systems, including hybrid search, multi-vector retrieval, and re-ranking pipelines.
• Design and implement knowledge graphs and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.
• Architect agentic workflows and multi-agent systems using Google Agent Development Kit (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.
• Design robust agent harnesses - governance, constraints, feedback loops, state/session management, and execution controls that make long-running agent systems reliable and safe.
• Integrate agents with tools and data via the Model Context Protocol (MCP) and orchestrate inter-agent collaboration and task delegation via the Agent2Agent (A2A) protocol.
• Support the integration of GenAI and agentic applications into production environments, ensuring robust deployment, scalability, observability, and maintainability.
• Contribute to the development and optimization of real-time and streaming AI solutions.
• Stay current with the latest advances in generative and agentic AI and actively share knowledge with the team.
• Ensure adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.
• Mentor junior team members, provide code reviews, and foster a culture of technical excellence.

Required Technical Skills

• Deep, hands-on expertise in core generative AI concepts - foundation models, LLMs, embeddings, tokenization, and context-window management.
• Advanced skills in prompt engineering and context engineering, including familiarity with prompt design tools/frameworks and dynamic context orchestration.
• Strong experience building RAG systems, including chunking strategies, hybrid search, and multi-vector retrieval.
• Practical experience designing knowledge graphs and Graph RAG pipelines (e.g., using graph databases such as Neo4j or ArangoDB) for relationship-aware, multi-hop retrieval.
• Proven experience building agentic AI systems with Google ADK and/or comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling, planning, and memory.
• Strong grasp of multi-agent orchestration patterns (supervisor/worker, hierarchical, peer-to-peer) and harness engineering - governance, feedback loops, execution controls, agent isolation/sandboxing.
• Hands-on experience with agent interoperability protocols - the Model Context Protocol (MCP) for tool/data access and the Agent2Agent (A2A) protocol for inter-agent collaboration.
• Experience with agent observability and evaluation (e.g., tracing, OpenTelemetry-based tooling) for production agent systems.
• Proficiency with major GenAI APIs (OpenAI, Gemini, Claude, etc.) and orchestration frameworks such as LangChain and LlamaIndex.
• Strong skills in NLP (NER, dependency parsing, text classification, topic modeling).
• Proficiency with vector databases and embedding models for large-scale retrieval.
• Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for AI/agentic applications.
• Solid understanding of AI compliance, guardrails, and responsible AI practices.
• Strong skills in Python and experience with data preprocessing, document ingestion, and API development.

Required Soft Skills

• Strong collaboration skills to work effectively in cross-functional teams.
• Analytical and proactive approach to problem-solving.