Middle Machine Learning Engineer – Financial Applications
INVESTBANQ TECH LAB LIMITED
- Алматы
- Постоянная работа
- Полная занятость
- research, design, code, test, and deploy machine learning models and services tailored for financial applications such as portfolio construction and market forecasting;
- take complete ownership of projects from concept through production, ensuring high-quality, scalable, and maintainable solutions;
- leverage state-of-the-art techniques to solve complex challenges in financial analytics;
- continuously research and implement new methodologies to enhance model accuracy and efficiency;
- design and deploy LLM-based and agentic systems for financial use cases (research, document intelligence, portfolio explanations, decision support);
- build RAG pipelines over structured and unstructured financial data;
- develop multi-step agentic workflows and tool-using agents with secure API and service integration;
- ensure robustness, auditability, and predictable behavior through guardrails and evaluation frameworks in regulated environments;
- collaborate with internal stakeholders to gather requirements, manage expectations, and translate business needs into technical solutions;
- communicate complex technical concepts clearly to non-technical audiences and ensure alignment with business objectives.
- degree (Bachelor’s, Master’s or Ph.D.) in Computer Science, Data Science, Machine Learning, Financial Engineering, or a related field.
- 3+ years of hands-on experience in AI, machine learning, or data science roles, with demonstrable experience in building and deploying production-grade models;
- prior exposure to the financial sector, especially in areas such as time series forecasting, portfolio optimization, and quantitative analysis.
- strong proficiency in coding with languages such as Python;
- extensive experience with machine learning frameworks (e.g., PyTorch, scikit-learn);
- solid background in data architecture, big data technologies, and cloud platforms (AWS, GCP, Azure);
- working knowledge of financial analytics, risk modeling, and quantitative finance methodologies;
- experience with large language models (LLMs) and frameworks such as LangChain, LlamaIndex, etc..
- experience with convex optimization techniques to refine portfolio management strategies;
- familiarity with financial econometrics or related quantitative finance methods.
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