Sr. AI/Python Engineer-Vice President

Engineer the Future of Global Finance

At Citi, our Tech team doesn’t just support finance—we are helping to redefine it. Every day, $5 trillion crosses through our network. We do business in 180+ countries operating at a scale few can match. From deploying advanced AI to helping shape global markets, we build systems that matter. We are seeking a skilled and innovative Vice President-level Machine Learning Systems Engineer with extensive ML Ops experience to join our team and lead the charge in building the next generation of intelligent financial systems.

Role Overview

In this senior tech lead role, you will be responsible for the architecture, construction, and maintenance of scalable backend systems that power our machine learning initiatives. You will own the deployment of data science (DS) models and design robust, enterprise-grade pipelines that enable the seamless integration of DS solutions into our core applications.

You will act as the crucial bridge between data science and production-ready systems, working closely with data scientists, front-end engineers, and business leaders. If you are passionate about operationalizing complex models, creating highly reliable backend services, and defining the lifecycle for data science workflows at an enterprise scale, this role is a perfect fit.

Key Responsibilities & Competencies

• Strategic Impact & Architectural Leadership: Take ownership of the ML systems architecture. Proactively assess and resolve complex, high-impact challenges by designing and evaluating multifaceted alternatives. Drive significant value by leading the development and deployment of robust analytical frameworks and ML pipelines that align with strategic business goals.
• Leadership & Influence: Exercise formal and informal leadership to foster a culture of technical excellence. Consistently mentor and develop emerging talent, guiding them in best practices for building scalable and resilient systems. Influence strategic decisions and drive project success through expert guidance, counsel, and facilitative leadership across significant, global projects.
• End-to-End ML Systems Delivery: Direct comprehensive analytics and ML initiatives designed to resolve critical business challenges. Orchestrate the entire system change lifecycle, from initial requirements gathering and project scoping through to implementation, deployment, and operational support.
• Stakeholder Engagement & Business Liaison: Demonstrate exceptional communication and diplomacy skills. Engage effectively with cross-functional stakeholders and business leaders to articulate complex technical concepts, negotiate solutions, and align objectives. Drive critical communication channels between business and IT to ensure strategic alignment.
• Operational Excellence & Quality Assurance: Directly impact the performance and operational throughput of multiple teams. Assure the quality of work output for individual contributions and cross-functional teams. Execute advanced data mining and analytical procedures to optimize operational processes and elevate data integrity standards.
• Risk Stewardship & Compliance: Demonstrate proactive risk assessment in all decisions, prioritizing the firm’s reputation and the integrity of its assets. Ensure stringent adherence to applicable laws, regulations, and corporate policies, exercising astute ethical judgment in all business practices.

Required Skills & Technical Qualifications

• Experience: 7+ years of professional experience implementing data-intensive, large-scale ML solutions, with a proven track record of technical leadership and using agile methodologies.
• Programming Languages:
  • Primary: Expert-level Python, Jupyter Notebook Scripting
  • Secondary: MLOps
• Machine Learning Frameworks & Libraries:
  • Core ML: Scikit-learn, Keras, TensorFlow, PyTorch
  • Advanced NLP & Generative AI:  RAGAs, DsPy, Large Language Models (LLM)
• MLOps & ML Lifecycle Management:
  • Platforms: MLFlow, Dagster, Google Cloud Vertex AI
• Database Management Systems:
  • Relational: Oracle, MySQL, PostgreSQL
  • NoSQL: MongoDB
• Infrastructure & DevOps:
  • CI/CD & DevOps Tooling: UDeploy, Harness, Tekton, Jenkins, Maven
  • Version Control: Git
  • Containerization & Orchestration: Docker, OpenShift, Amazon ECS
• Data Engineering & Processing:
  • Messaging & Asynchronous Communication: K…