Senior Machine Learning Engineer, Vice President

Citi • Tampa, FL

1d

About The Position

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.

Requirements

• 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.
• 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.
• Demonstrated ability to design scalable, fault-tolerant, and performant ML systems.
• Exceptional analytical, interpretive, and problem-solving skills with a focus on engineering challenges and innovative solutions.
• Excellent interpersonal, verbal, and written communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Proven ability to work independently, drive projects to completion, and provide technical leadership and mentorship

Nice To Haves

• Experience with graph neural networks, graph databases, or distributed systems for ML.

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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed