Data Engineer

• As a Data Engineer, you will be involved, but not limited to:

• 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 technology SDLC process.

• Implement features through the ML lifecycle (Development, Testing, Training, Production, Monitoring/Evaluation) to ensure scalability and reliability.

• Design and develop algorithms for generative models using deep learning techniques

Qualifications:

• 5+ years of industry experience as a data engineer, specializing in ML Modeling, Ranking, Recommendations, or Personalization systems.

• 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 (3+ years) in Python, SQL, Spark, PySpark, TensorFlow and other analytical/model-building programming languages.

• Proficiency with tools and LLMs.

• Ability to work independently and collaboratively within a team.

Preferred Skills:

• Experience in GenAI/LLMs projects.

• Familiarity with distributed data/computing tools (e.g., Hadoop, Hive, Spark, MySQL).

• Background in investment banking and risk management.

• Neo4j experience