Senior AI/ML Engineer – Full Stack Developer

Citi

Senior AI/ML Engineer developing AI systems and Agentic AI solutions for financial services at Citi. Leading engineering teams in designing and implementing AI tools enhancing operational efficiency.

Posted 6/10/2026full-timeIrving • Florida, Texas • 🇺🇸 United StatesSenior💰 $125,760 - $188,640 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCassandraCloudDistributed SystemsDockerDynamoDBGoogle Cloud PlatformJavaKerasKubernetesMicroservicesMongoDBMySQLNoSQLNumpyOpenShiftOraclePandasPostgresPythonPyTorchSpringSpring BootSpringBootSQLTensorflow

About the role

Key responsibilities & impact

Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions)
Utilize in-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications
Review and analyze proposed technical solutions for projects
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.

Requirements

What you’ll need

At least 6+ years of progressive experience in software engineering and AI/ML development
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
Expert proficiency in Python for AI/ML development, including data manipulation (Pandas), scientific computing (NumPy), and machine learning frameworks
Strong proficiency in Java (e.g., Spring Boot, Microservices, Enterprise Integration Patterns, RESTful APIs)
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)
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
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
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)
Solid understanding of AI compliance, guardrails, Responsible AI practices, and enterprise security standards within a highly regulated environment.

Benefits

Comp & perks

medical, dental & vision coverage
401(k)
life, accident, and disability insurance
wellness programs
paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonJavaAI/ML developmentLarge Language Models (LLMs)Generative AIAgentic AINatural Language Processing (NLP)CI/CDcontainerizationorchestration

Soft Skills

leadershipmentoringcollaborationcommunicationtechnical excellencecontinuous improvementproblem-solvingorganizational skillsperformance evaluationteam management