Job Description

We are seeking a highly skilled and experienced Assistant Vice President (AVP), Data Science & AIML Engineer, to join our growing team. The ideal candidate will possess a strong blend of data science expertise, machine learning engineering capabilities, and proven hands-on experience in developing and deploying AI/ML solutions in a production environment. This role requires deep proficiency in Python, a solid understanding of CI/CD pipelines, and experience building high-performance APIs, particularly with FastAPI. You will be instrumental in designing, building, and deploying advanced analytical models and machine learning systems that address complex business challenges.

Key Responsibilities:

Model Development:

Design, develop, and implement advanced machine learning models (e.g., predictive, prescriptive, generative AI) to solve complex business problems, from initial data exploration and feature engineering to model training and evaluation.

MLOps & Deployment:

Lead the deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance.

API Development:

Build and maintain robust, high-performance APIs (using frameworks like FastAPI) to serve machine learning models and integrate them with existing applications and systems.

CI/CD Implementation:

Establish and manage continuous integration and continuous deployment (CI/CD) pipelines for ML code and model deployments, promoting automation and efficiency.

Data Engineering:

Collaborate with data engineers to ensure optimal data pipelines and data quality for model development and deployment.

Experimentation & Optimization:

Conduct rigorous experimentation, A/B testing, and model performance monitoring to continuously improve and optimize AI/ML solutions.

Code Quality & Best Practices:

Promote and enforce best practices in software development, including clean code, unit testing, documentation, and version control.

Technical Leadership:

Mentor junior team members, contribute to technical discussions, and drive the adoption of new technologies and methodologies within the team.

Stakeholder Communication:

Effectively communicate complex technical concepts and model results to both technical and non-technical stakeholders.

Required Skills & Qualifications:

Education:

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field.

Experience:

+ Minimum of 6+ years of professional experience in Data Science, Machine Learning Engineering, or a similar role, with a strong track record of deploying ML models to production.
+ Proven experience in a lead or senior technical role.

Technical Proficiency:

+

Python:

Expert-level proficiency in Python programming, including experience with relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn) and deep learning frameworks (e.g., TensorFlow, PyTorch).
+

FastAPI:

Strong hands-on experience designing, developing, and deploying RESTful APIs using FastAPI.
+

CI/CD:

Solid understanding and practical experience with CI/CD tools and methodologies (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps) for MLOps.
+

MLOps:

Experience with MLOps platforms, model monitoring, and model versioning.
+

Cloud Platforms:

Experience with at least one major cloud provider (e.g., AWS, Azure, GCP) for deploying and managing ML workloads.
+

Database Skills:

Proficiency in SQL and experience working with relational and/or NoSQL databases
+

Machine Learning:

Deep understanding of machine learning algorithms, statistical modeling, and data mining techniques.
+

Problem Solving:

Excellent analytical and problem-solving skills, with the ability to translate complex business problems into actionable data science solutions.
+

Communication:

Strong verbal and written communication skills, with the ability to articulate technical concepts to diverse audiences.

Preferred Skills & Qualifications

Experience with containerization technologies (e.g., Docker, Kubernetes). Familiarity with big data technologies (e.g., Spark, Hadoop). Experience in the financial services industry. Knowledge of generative AI techniques and large language models (LLMs). Contributions to open-source projects or relevant publications.


This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.


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Job Family Group:

Technology
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Job Family:

Applications Development
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Time Type:

Full time
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Most Relevant Skills

Please see the requirements listed above.
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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.
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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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