Digital Software Engineer Lead Analyst - VP

Discover your future at Citi

Job Overview

Key Responsibilities

• Lead the design, development, and implementation of sophisticated AI/ML, Generative AI, and Agentic AI solutions, including AI assistant tools, in close collaboration with AI architects, product owners, and cross-functional engineering teams within the Engineering Excellence framework.
• Architect and develop end-to-end AI systems, leveraging both Python for machine learning workflows and Java for scalable enterprise-grade backend services, ensuring seamless integration with existing and new applications.
• Design and implement intelligent agentic systems that exhibit autonomous decision-making capabilities, enabling advanced automation and self-optimizing processes.
• Develop, fine-tune, and optimize Large Language Models (LLMs) using both parameter-efficient techniques and full fine-tuning, focusing on their integration into robust, production-ready systems supported by both Python and Java components.
• Drive the implementation and experimentation with advanced generative AI methods such as prompt engineering and Retrieval-Augmented Generation (RAG), ensuring their effective deployment and performance in enterprise environments.
• Own the deployment pipeline for AI models and associated services, leveraging CI/CD methodologies, containerization (Docker), and orchestration (Kubernetes) on leading cloud platforms (AWS, Azure, GCP) to ensure secure, scalable, and automated releases.
• Apply deep knowledge of database technologies (SQL and NoSQL) to design efficient data storage, retrieval, and management strategies for AI applications, ensuring data integrity and performance.
• Contribute to the establishment and enforcement of engineering best practices, architectural patterns, and tooling standards for full-stack AI development, advocating for maintainability, observability, and cost-efficiency.
• Stay abreast of the latest advancements in AI/ML, Agentic AI, cloud technologies, and DevOps trends, proactively sharing knowledge and driving adoption of innovative solutions.
• Ensure strict adherence to ethical AI guidelines, data privacy regulations, and compliance standards throughout the entire AI solution lifecycle.
• Act as a mentor for junior engineers, providing expert guidance on Python, Java, cloud technologies, and CI/CD best practices, fostering a culture of technical excellence and continuous improvement.

Required Technical Skills

• Polyglot Programming Expertise: Python: Expert proficiency in Python for AI/ML development, including data manipulation (Pandas), scientific computing (NumPy), and machine learning frameworks. Java: Strong proficiency in Java (e.g., Spring Boot, Microservices, Enterprise Integration Patterns, RESTful APIs) for building scalable, high-performance, and resilient enterprise applications.
• Python: Expert proficiency in Python for AI/ML development, including data manipulation (Pandas), scientific computing (NumPy), and machine learning frameworks.
• Java: Strong proficiency in Java (e.g., Spring Boot, Microservices, Enterprise Integration Patterns, RESTful APIs) for building scalable, high-performance, and resilient enterprise applications.
• AI/ML & Agentic AI: Extensive experience with Generative AI, Agentic AI principles, and the development of AI assistant tools. Hands-on experience with LLMs and fine-tuning methods (e.g., LoRA, QLoRA, Adapter/Prefix Tuning, instruction tuning). Practical knowledge of model optimization techniques (e.g., compression, quantization) and familiarity with tools such as DeepSpeed, vLLM, GPTQ, or similar. Proficient in prompt engineering, prompt design tools/frameworks, and building robust RAG systems (hybrid search, multi-vector retrieval). Proficient with machine learning frameworks (PyTorch, TensorFlow, Keras) and distributed training. Strong skills in Natural Language Processing (NLP) techniques (NER, Dependency Parsing, Text Classification, Topic Modeling), transfer learning, and advanced learning paradigms. Familiarity with generative AI tools and libraries like LangChain, LlamaIndex, Hugging Face, and major GenAI APIs (e.g., OpenAI, Gemini, Claude, AWS Bedrock).
• Extensive experience with Generative AI, Agentic AI principles, and the development of AI assistant tools.
• Hands-on experience with LLMs and fine-tuning methods (e.g., LoRA, QLoRA, Adapter/Prefix Tuning, instruction tuning).
• Practical knowledge of model optimization techniques (e.g., compression, quantization) and familiarity with tools such as DeepSpeed, vLLM, GPTQ, or similar.
• Proficient in prompt engineering, prompt design tools/frameworks, and building robust RAG systems (hybrid search, multi-vector retrieval).
• Proficient with machine learning frameworks (PyTorch, TensorFlow, Keras) and distributed training.
• Strong skills in Natural Language Processing (NLP) techniques (NER, Dependency Parsing, Text Classification, Topic Modeling), transfer learning, and advanced learning paradigms.
• Familiarity with generative AI tools and libraries like LangChain, LlamaIndex, Hugging Face, and major GenAI APIs (e.g., OpenAI, Gemini, Claude, AWS Bedrock).
• Database Technologies: Solid experience with both SQL (e.g., PostgreSQL, Oracle, MySQL) and NoSQL (e.g., MongoDB, Cassandra, DynamoDB) databases, including schema design, query optimization, and integration with applications.
• Solid experience with both SQL (e.g., PostgreSQL, Oracle, MySQL) and NoSQL (e.g., MongoDB, Cassandra, DynamoDB) databases, including schema design, query optimization, and integration with applications.
• Cloud Platforms: Extensive hands-on experience with at least one major cloud provider (AWS, Azure, or GCP), including services for compute, storage, networking, AI/ML, and data.
• Extensive hands-on experience with at least one major cloud provider (AWS, Azure, or GCP), including services for compute, storage, networking, AI/ML, and data.
• CI/CD & DevOps: Strong understanding and practical experience with CI/CD pipelines, automated testing, infrastructure as code (IaC), and configuration management. Expertise with containerization (Docker) and orchestration (Kubernetes, OpenShift). Familiarity with monitoring, logging, and alerting tools for production systems.
• Strong understanding and practical experience with CI/CD pipelines, automated testing, infrastructure as code (IaC), and configuration management.
• Expertise with containerization (Docker) and orchestration (Kubernetes, OpenShift).
• Familiarity with monitoring, logging, and alerting tools for production systems.
• Security & Compliance: Solid understanding of AI compliance, guardrails, Responsible AI practices, and enterprise security standards within a highly regulated environment.
• Solid understanding of AI compliance, guardrails, Responsible AI practices, and enterprise security standards within a highly regulated environment.

Required Soft Skills

• Exceptional collaboration and communication skills, capable of effectively bridging the gap between diverse technical teams (AI/ML, Java, DevOps, Cloud) and non-technical stakeholders.
• Proactive and analytical problem-solver, adept at navigating complex technical challenges and driving innovative, cross-domain solutions in a dynamic environment.
• Ability to clearly articulate complex technical concepts, designs, and solutions to diverse audiences, both technical and non-technical.
• Strong passion for continuous learning, innovation, and mentoring, contributing significantly to a culture of technical excellence and organizational transformation.

Qualifications

• At least 6+ years of progressive experience in software engineering and AI/ML development, with a minimum of 5 years specifically focused on Generative AI, Agentic AI, and full-stack AI solutions.
• Demonstrated portfolio of successful, impactful projects leveraging Python, Java, cloud services, and CI/CD/DevOps practices in an enterprise setting.
• Extensive experience working with large-scale distributed systems and architecting solutions for high availability and performance.

Education

Primary Location:

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Primary Location Full Time Salary Range:

• In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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Most Relevant Skills

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Automated Processing and AI