Senior AI ML Engineer

• 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
• Lead the deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance
• Build and maintain robust, high-performance APIs (using frameworks like FastAPI) to serve machine learning models and integrate them with existing applications and systems
• Establish and manage continuous integration and continuous deployment (CI/CD) pipelines for ML code and model deployments, promoting automation and efficiency
• Collaborate with data engineers to ensure optimal data pipelines and data quality for model development and deployment
• Conduct rigorous experimentation, A/B testing, and model performance monitoring to continuously improve and optimize AI/ML solutions
• Promote and enforce best practices in software development, including clean code, unit testing, documentation, and version control
• Mentor junior team members, contribute to technical discussions, and drive the adoption of new technologies and methodologies within the team
• Effectively communicate complex technical concepts and model results to both technical and non-technical stakeholders.

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field
• 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
• 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)
• Strong hands-on experience designing, developing, and deploying RESTful APIs using FastAPI
• Solid understanding and practical experience with CI/CD tools and methodologies (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps) for MLOps
• Experience with MLOps platforms, model monitoring, and model versioning
• Experience with at least one major cloud provider (e.g., AWS, Azure, GCP) for deploying and managing ML workloads
• Proficiency in SQL and experience working with relational and/or NoSQL databases
• Deep understanding of machine learning algorithms, statistical modeling, and data mining techniques
• Excellent analytical and problem-solving skills, with the ability to translate complex business problems into actionable data science solutions
• Strong verbal and written communication skills, with the ability to articulate technical concepts to diverse audiences.

• 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.

Not explicitly stated.