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:
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Job Family:
Applications Development------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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