Middle Machine Learning Engineer – Financial Applications

Investbanq is an AI-powered wealth operating system for family offices, asset managers and banks. We are empowering traditional financial institutions to rapidly transform into AI-driven WealthTech players, enabling them to effectively capitalize on the growing wealth transfer to affluent millennials and other digital natives across emerging Asia and MENA. We are scaling quickly across Asia and MENA and are assembling a high‑calibre engineering team to power our next phase of growth.

Job overview

We are looking for a technically skilled and entrepreneurial Middle Machine Learning Engineer to drive the development of advanced AI and machine learning solutions for financial applications. You will take charge of building, deploying, and maintaining innovative models for portfolio optimization, forecasting, and risk management. In this role, you will manage the entire product development lifecycle—from writing extensive code and designing robust algorithms to ensuring that project milestones and stakeholder expectations are met.

Key responsibilities

End-to-end product development

• 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;

Technical innovation & problem solving

• 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;

LLM-powered & Agentic AI Systems

• 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;

Team engagement

• 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.

Qualifications

Education

• degree (Bachelor’s, Master’s or Ph.D.) in Computer Science, Data Science, Machine Learning, Financial Engineering, or a related field.

Experience

• 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.

Technical skills (must have)

• 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..

Good to have

• experience with convex optimization techniques to refine portfolio management strategies;
• familiarity with financial econometrics or related quantitative finance methods.

Working conditions

• hybrid work environment: 3 days in the office, 2 days remote;

• modern office location in Almaty;

• official employment in compliance with AIFC Employment Regulations;

• opportunities for professional growth and career progression;

• multilingual working environment;

• exposure to cutting-edge AI-driven WealthTech solutions;

• a collaborative and inclusive corporate culture that values innovation and initiative;

• periodic remote (e.g., during winter) from year 2;

• ESOP participation from year 2.