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Strong employer brand plus mid-level ML role and popular LLM skills increase applicant competition.
Core ML skills are transferable, but finance domain and portfolio-specific knowledge raise sensitivity to background.
Explicit 4+ years and a long list of mandatory ML/LLM frameworks and tools raise filter strictness.
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Design and develop generative AI and machine learning solutions to solve fixed income investment and business problems.
Build, train, fine-tune, and deploy machine learning and deep learning models integrated into production systems with scalable pipelines and APIs.
Collaborate closely with portfolio managers, researchers, and business stakeholders to translate investment challenges into effective data science solutions, ensuring responsible AI practices and clear communication of insights.
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
4+ years of relevant experience in AI/ML roles.
Proficiency in Python, SQL, ML frameworks (PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain), and familiarity with data tools (Pandas, Snowflake, FAISS).
Experience with cloud platforms (GCP, Azure, AWS) and version control systems (e.g., Git/GitHub).
Strong foundation in ML algorithms, linear algebra, probability, statistics, and optimization theory with domain knowledge in generative AI, statistical learning, NLP, deep learning, or time series analysis.
Experienced operating in collaborative environments bridging AI development with financial portfolio management and quantitative research.
Prior exposure to financial services or large-scale enterprise systems preferred but not mandatory.