Lead Software Engineer — Enterprise Applications
Citigroup
Role Overview Citi's Banking Technology organization is seeking a highly hands-on Lead Software Engineer to design, build, and deploy cutting-edge AI solutions for Investment, Corporate, and Commercial Banking.
This role focuses on implementing
scalable, agentic AI frameworks and generative AI solutions
within small, agile squads. The successful candidate will operate with an
AI-first mindset , emphasizing rapid prototyping, MVP-driven development, and iterative delivery of production-grade AI capabilities.
Key Responsibilities
AI Solution Development:
Design, implement, and deploy scalable agentic AI frameworks and generative AI solutions for critical business use cases, ensuring robustness, performance, security, and reliability.
Agentic Systems:
Develop and integrate agentic AI systems leveraging multiple model providers and platforms (e.g., OpenAI, Anthropic, Google APIs).
Full-Stack AI Engineering:
Build full-stack applications integrating LLM-driven workflows and AI coding tools (e.g., Devin, GitHub Copilot).
Iterative Delivery:
Drive an MVP-first, rapid-iteration approach with continuous experimentation and improvement.
Evaluation & Optimization:
Define and implement metrics, evaluation strategies, and feedback loops to continuously improve AI system performance and behavior.
Emerging Technologies:
Research, prototype, and integrate advances in agent-based, autonomous, and generative AI technologies.
Technical Collaboration:
Contribute hands-on within cross-functional teams and provide technical guidance and mentorship to junior engineers.
Domain Alignment:
Ensure AI solutions are well-aligned to banking and financial services requirements, constraints, and business outcomes.
Qualifications
Programming: Strong proficiency in Python and/or Java (Spring Boot); working knowledge of JavaScript/TypeScript (Angular, Node.js). Full-stack experience is a plus.
AI/ML Expertise
Solid understanding
of core AI concepts such as knowledge representation, planning, and multi-agent systems.
Hands-on experience with LLMs, RAG, prompt engineering, MCPs, and agent frameworks (e.g., Google ADK).
Practical experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas).
Familiarity with AI coding tools such as Devin, Claude Code, GitHub Copilot, and Antigravity.
Software Engineering
Strong grounding in modern engineering practices including Git, CI/CD, testing, code reviews, agile delivery, application resiliency, and security.
Architecture
Experience designing API-first, microservices-based and event-driven architectures, including data engineering patterns for AI systems.
Platforms
Hands-on experience with Docker, Kubernetes, and OpenShift.
Problem Solving & Communication
Strong analytical skills with the ability to clearly communicate complex technical concepts.
Domain Knowledge
Solid understanding of
#J-18808-Ljbffr
This role focuses on implementing
scalable, agentic AI frameworks and generative AI solutions
within small, agile squads. The successful candidate will operate with an
AI-first mindset , emphasizing rapid prototyping, MVP-driven development, and iterative delivery of production-grade AI capabilities.
Key Responsibilities
AI Solution Development:
Design, implement, and deploy scalable agentic AI frameworks and generative AI solutions for critical business use cases, ensuring robustness, performance, security, and reliability.
Agentic Systems:
Develop and integrate agentic AI systems leveraging multiple model providers and platforms (e.g., OpenAI, Anthropic, Google APIs).
Full-Stack AI Engineering:
Build full-stack applications integrating LLM-driven workflows and AI coding tools (e.g., Devin, GitHub Copilot).
Iterative Delivery:
Drive an MVP-first, rapid-iteration approach with continuous experimentation and improvement.
Evaluation & Optimization:
Define and implement metrics, evaluation strategies, and feedback loops to continuously improve AI system performance and behavior.
Emerging Technologies:
Research, prototype, and integrate advances in agent-based, autonomous, and generative AI technologies.
Technical Collaboration:
Contribute hands-on within cross-functional teams and provide technical guidance and mentorship to junior engineers.
Domain Alignment:
Ensure AI solutions are well-aligned to banking and financial services requirements, constraints, and business outcomes.
Qualifications
Programming: Strong proficiency in Python and/or Java (Spring Boot); working knowledge of JavaScript/TypeScript (Angular, Node.js). Full-stack experience is a plus.
AI/ML Expertise
Solid understanding
of core AI concepts such as knowledge representation, planning, and multi-agent systems.
Hands-on experience with LLMs, RAG, prompt engineering, MCPs, and agent frameworks (e.g., Google ADK).
Practical experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas).
Familiarity with AI coding tools such as Devin, Claude Code, GitHub Copilot, and Antigravity.
Software Engineering
Strong grounding in modern engineering practices including Git, CI/CD, testing, code reviews, agile delivery, application resiliency, and security.
Architecture
Experience designing API-first, microservices-based and event-driven architectures, including data engineering patterns for AI systems.
Platforms
Hands-on experience with Docker, Kubernetes, and OpenShift.
Problem Solving & Communication
Strong analytical skills with the ability to clearly communicate complex technical concepts.
Domain Knowledge
Solid understanding of
#J-18808-Ljbffr
Reference: WJ-3875_13328530