Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
About the role
- ML/AI Engineer designing machine learning models and algorithms for enterprise-level AI products at a leading bank. Solving complex financial problems through innovative data science techniques.
Responsibilities
- Design and develop machine learning models (Supervised, Unsupervised, and Reinforcement Learning), AI (Generative models and agent orchestration) models, and deep learning models (e.g., Neural Networks and autoencoders)
- Run machine learning tests and experiments
- Train and retrain systems to prevent drift and optimize results
- Solve complex problems with multi-layered data sets, extend existing ML frameworks (Scikit-Learn, XGBoost, Tensorflow) and AI frameworks (Keras, LangChain)
- Leverage and develop advanced analytics models (network based, forecasting, rules-based), implement said algorithms, and build tools to apply them
- Turn structured, semi-structured and unstructured data into useful information
- Develop ML/AI algorithms to analyze huge volumes of historical data to derive insights, make decisions, and form predictions
- Run tests, perform statistical analysis, and interpret test results
- Contribute to shaping the digital foundations: (Hypergraph) Scenario Engine and Network based Methods: graph-based modeling tool that maps relationships between entities and simulates cascading scenarios; Chatbots (i.e., Distribution); Semantic Engine: AI layer that enables meaning-based search as opposed to keyword search
- Conduct large-scale analysis of information to discover patterns and trends by combining different models and algorithms
- Get insights from data (descriptive, attribution)
- Perform Topological Data Modeling, Causality, Variable Importance Analysis, Attribution modeling, Regression
Requirements
- Master’s or Ph.D. in Mathematics, Statistics, Computer Science, Data Science, Physics, AI, Machine Learning or a related field
- Experience in model development (ML/ data science, AI/GenAI) within financial services or technology sectors
- Proficiency in Python and SQL, TensorFlow, PyTorch, XGBoost, scikit-learn
- Strong grasp of artificial intelligence and machine learning frameworks and stacks
- Familiarity with cloud platforms (AWS, Azure, GCP) and CI/CD pipelines is advantageous
- Intellectual curiosity and adaptability to emerging AI and quant finance trends
- Strong communication skills to explain complex models to non-technical stakeholders
- Ability to work independently and collaboratively in a fast-paced, multidisciplinary environment
- Attention to detail and a rigorous approach to model validation and testing
Benefits
- Health insurance
- Tuition reimbursement
- Accident and life insurance
- Retirement savings plans
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