Senior Python Engineer
Senior Python Engineer
Posted yesterday
SENIORITY
Senior
About the role
- Master's degree/bachelor's degree in computer science, Data Science, Statistics, Mathematics, Engineering or related field.
- Required Qualifications/Skills/Experience 5+ years of industry experience as a backend Python developer or data scientist.5+ years of experience designing and developing scalable and reliable machine learning systems for training, inference, monitoring, and iteration
- Strong background of ML/DL/LLM algorithms, model architectures, and training techniques
- Proficiency in Python, SQL, Spark, PySpark, TensorFlow or other analytical/model-building programming languages
- Proficiency with tools and LLMsAbility to work independently and collaboratively within a team.
- Preferred Qualifications/Skills/Experience
- Experience in GenAI/LLMs projects
- Familiarity with distributed data/computing tools (e.g., Hadoop, Hive, Spark, MySQL)
- Background in financial business, like banking, risk management
- Should be familiar with capital markets, financial instruments and modeling
- This is a development position for establishing and implementing new or revised applications and programs in the Technology team
- Responsible for data extraction and data analysis from structured and unstructured sources
- Develop systems to clean results to build predictive and prescriptive models and implement them in a production environment by partnering with technology and business partners
- Address complex problems involving financial data with a specific focus on credit risk management
- Requires an open and adaptive mindset to learn new and advanced models in LLM and GenAI and bring in innovative solutions to complex business problems
- Develop plans and coordinate with teams for all analytical efforts
- Manage deliverables in an agile environment and maintain clear communication with all model stakeholders
- Present status, issues, and analytical findings to various audience groups like business, technology management, risk review, model governance, etc.
- Data modelling and cleaning from internal and external sources
- Build predictive and prescriptive models by manipulating and cleaning results
- Develop, manage, and deploy analytical solutions using Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) to production systems using the SDLC process
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Your notice period
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