Senior AI ML Engineer - Assistant Vice President

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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