Senior Machine Learning Engineer, Vice President

Location: Tampa, Florida, United States

Employment Type: Regular

Candidate should possess deep hands-on expertise in designing, building, and deploying scalable machine learning systems, including advanced NLP and Generative AI (LLM) solutions. This position demands strong technical leadership, a quick learning ability, a proven track record in delivering high-value, production-grade AI solutions, and the capacity to mentor junior engineers.

The right candidate will be expected to be a key player in the project evolution & deployment shouldering the following responsibilities:

Work as a collaborative member of a team spread over multiple locations (India, UK, US) Understand internally published architectural guidelines to design solutions and represent them in architectural reviews. Define & communicate development standards that follow established architectural designs and perform code reviews to ensure quality standards of systems & team. Lead by example in developing exceptional quality code by doing design & code reviews. Design & develop platform functionality that is scalable & configurable as a global platform.

Key Responsibilities

• ML System Design & Architecture: Lead the design and architecture of robust, scalable, and high-performance machine learning systems, ensuring seamless integration with existing platforms.
• Production ML Model Deployment: Own the end-to-end lifecycle of deploying and operationalizing machine learning models in production environments, ensuring efficiency, reliability, and maintainability.
• Advanced AI/ML Engineering: Develop, optimize, and implement advanced machine learning algorithms and statistical models, focusing on engineering best practices for performance and scalability.
• Generative AI & NLP System Development: Engineer and integrate cutting-edge Generative AI (LLM) and Natural Language Processing (NLP) solutions. This includes designing efficient prompting strategies, developing LLM-based data augmentation techniques, and implementing Retrieval-Augmented Generation (RAG, including advanced RAG) to enhance model capabilities within production systems.
• Deep Learning Infrastructure: Design and build systems to effectively apply and deploy deep learning techniques (ANN, LSTM, CNN, BERT, XLNet, Transformers, neural & LLM-based embeddings) for state-of-the-art AI applications at scale.
• MLOps & Automation: Establish and implement MLOps practices, including CI/CD pipelines, automated testing, monitoring, and retraining strategies for ML models to ensure continuous improvement and stability.
• Performance Optimization: Optimize ML models and underlying infrastructure for computational efficiency, speed, and resource utilization.
• Technical Leadership & Mentorship: Drive technical excellence, promote best coding practices, perform code reviews, and provide mentorship to junior engineers.
• Cross-Functional Collaboration: Partner closely with data scientists, product managers, and other engineering teams to translate complex business requirements into technical ML solutions and ensure successful delivery.
• Risk Management & Compliance: Integrate risk assessment and compliance considerations into ML system design and deployment, ensuring adherence to applicable laws, regulations, and internal policies to safeguard the firm's reputation and assets.

Qualifications

• Experience:
  • 5+ years of hands-on experience in Machine Learning Engineering, MLOps, or AI system development.
  • Minimum of 2 years of direct experience in engineering and deploying Generative AI/LLM solutions in production.
• Technical Skills:
  • Deep proficiency in Python for production-grade ML development, with expertise in relevant libraries (scikit-learn, pandas, SpaCy, TensorFlow, PyTorch, Hugging Face Transformers).
  • Strong experience with PySpark for large-scale data processing and building robust data pipelines.
  • Proficiency in big data frameworks (Hadoop, Spark, Hive, Hue) and experience with streaming technologies.
  • Extensive experience with MLOps tools and practices (e.g., Docker, Kubernetes, MLflow, Airflow, CI/CD for ML).
  • Proven experience in designing, implementing, and deploying NLP and deep learning models to production.
  • Hands-on experience with Generative AI development, including engineering prompting strategies, RAG implementation, and LLM fine-tuning and integration (e.g., Langchain, LlamaIndex).
  • Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services.
  • Good to have: Experience with graph neural networks, graph databases, or distributed systems for ML.
• System Design & Architecture: Demonstrated ability to design scalable, fault-tolerant, and performant ML systems.
• Problem-Solving: Exceptional analytical, interpretive, and problem-solving skills with a focus on engineering challenges and innovative solutions.
• Communication: Excellent interpersonal, verbal, and written communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Autonomy & Leadership: Proven ability to work independently, drive projects to completion, and provide technical leadership and mentorship

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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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Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.

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