Quant AI Engineer
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Key skills for this role
About the Role
MultiBank Group seeks a Quant AI Engineer to design, develop, and deploy AI-powered quantitative systems for trading, risk management, and market surveillance. The role combines quantitative development, machine learning engineering, and software engineering to build scalable production-grade AI solutions.
Key Skills for This Role
Responsibilities
- Develop machine learning and quantitative models for forecasting, risk analytics, pricing, anomaly detection, and market intelligence
- Design and implement predictive models using statistical, machine learning, and deep learning techniques
- Optimize model accuracy, reliability, and performance across production environments
- Build frameworks for model evaluation, experimentation, and continuous improvement
- Build scalable machine learning pipelines for model training, deployment, monitoring, and lifecycle management
- Develop APIs, services, and infrastructure that expose AI and quantitative capabilities to business applications
- Implement MLOps practices including model versioning, monitoring, governance, and automated deployment
- Ensure high availability, scalability, and observability of AI systems
- Design and implement LLM powered applications for research automation, knowledge extraction, decision support, and operational workflows
- Develop Retrieval Augmented Generation (RAG) architectures using internal and external datasets
- Build AI agents and workflow orchestration solutions that automate complex business processes
- Integrate AI solutions with trading platforms, market data feeds, risk systems, and enterprise applications
Requirements
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Quantitative Finance, or related discipline
- 5–10 years of experience in AI engineering, machine learning engineering, quantitative development, or software engineering
- Strong software engineering experience using Python
- Experience with machine learning frameworks including PyTorch, TensorFlow, Scikit learn, and XGBoost
- Strong understanding of machine learning engineering, model deployment, and MLOps practices
- Experience building production grade AI and machine learning systems
- Knowledge of LLM frameworks such as LangChain, LangGraph, LlamaIndex, OpenAI APIs, or AWS Bedrock
- Experience building RAG systems, vector database solutions, and AI agents
- Experience with SQL, Spark, Kafka, Docker, Kubernetes, and CI/CD pipelines
- Familiarity with AWS, Azure, or GCP cloud platforms
- Experience developing production systems within financial services, fintech, brokerage, capital markets, crypto, or other high performance environments is highly desirable
Full Job Posting
Role Overview
- We are seeking a Quant AI Engineer to design, develop, and deploy AI powered quantitative systems that support trading, risk management, market surveillance, forecasting, and operational intelligence.
- The successful candidate will combine quantitative development, machine learning engineering, and software engineering expertise to build scalable production grade AI solutions.
- This role is focused on transforming quantitative models and AI research into reliable, high performance systems capable of operating in real time financial environments.
Key Responsibilities
- Develop machine learning and quantitative models for forecasting, risk analytics, pricing, anomaly detection, and market intelligence.
- Design and implement predictive models using statistical, machine learning, and deep learning techniques.
- Optimize model accuracy, reliability, and performance across production environments.
- Build frameworks for model evaluation, experimentation, and continuous improvement.
- Build scalable machine learning pipelines for model training, deployment, monitoring, and lifecycle management.
- Develop APIs, services, and infrastructure that expose AI and quantitative capabilities to business applications.
- Implement MLOps practices including model versioning, monitoring, governance, and automated deployment.
- Ensure high availability, scalability, and observability of AI systems.
- Design and implement LLM powered applications for research automation, knowledge extraction, decision support, and operational workflows.
- Develop Retrieval Augmented Generation (RAG) architectures using internal and external datasets.
- Build AI agents and workflow orchestration solutions that automate complex business processes.
- Evaluate emerging AI technologies and integrate them into production systems where appropriate.
Technical Requirements
- Strong software engineering experience using Python.
- Experience with machine learning frameworks including PyTorch, TensorFlow, Scikit learn, and XGBoost.
- Strong understanding of machine learning engineering, model deployment, and MLOps practices.
- Experience building production grade AI and machine learning systems.
- Knowledge of LLM frameworks such as LangChain, LangGraph, LlamaIndex, OpenAI APIs, or AWS Bedrock.
- Experience building RAG systems, vector database solutions, and AI agents.
- Experience with SQL, Spark, Kafka, Docker, Kubernetes, and CI/CD pipelines.
- Familiarity with AWS, Azure, or GCP cloud platforms.
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Quantitative Finance, or a related discipline.
- 5–10 years of experience in AI engineering, machine learning engineering, quantitative development, or software engineering.
- Experience developing production systems within financial services, fintech, brokerage, capital markets, crypto, or other high performance environments is highly desirable.
- Understanding of trading systems, market data, pricing, risk management, and quantitative finance concepts is preferred.
- Strong analytical, engineering, and problem solving capabilities.
Why Join Us?
- Work with an industry leading global financial institution.
- Competitive salary and comprehensive employee benefits.
- Opportunities for professional growth and career advancement.
- Collaborative, inclusive, and dynamic work environment.
- Commitment to innovation and professional excellence.
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