AI/ML Engineer (Agentic AI & Data Science)

We are seeking an AI/ML Engineer with strong Data Science and Generative AI capabilities to design and deliver next-generation AI solutions across Global Financial Markets.

The role will focus on developing Agentic AI platforms, recommendation engines, retrieval systems, and predictive models supporting initiatives such as market intelligence, product recommendations, client engagement, workflow automation, and knowledge management.

You will work closely with business stakeholders, product owners, data scientists, and software engineers to translate business challenges into scalable AI-powered solutions.

• Design and develop AI agents using modern agent frameworks.
• Build and optimize RAG (Retrieval-Augmented Generation) solutions.
• Develop agent orchestration workflows and tool-calling frameworks.
• Implement prompt engineering, evaluation, reflection, and memory capabilities.
• Build reusable AI components that can be leveraged across multiple business use cases.

• Develop machine learning models for:
  • Client propensity prediction
  • Recommendation systems
  • Classification and ranking
  • Behavioral analytics
  • Next-best-action recommendations
• Perform data exploration, feature engineering, and model evaluation.
• Analyze large structured and unstructured datasets to generate actionable insights.
• Monitor model performance and continuously improve accuracy and relevance.

• Build production-ready AI services and APIs.
• Integrate AI solutions with enterprise systems and data sources.
• Implement monitoring, observability, and evaluation frameworks.
• Optimize AI solutions for performance, scalability, and cost efficiency.

Experience in Machine Learning Engineering, Data Science, AI Engineering, Advanced Analytics

Hands‑on experience building and deploying ML or AI solutions into production.

• Python (mandatory)
• SQL
• REST APIs

• Scikit-Learn
• XGBoost / LightGBM
• TensorFlow or PyTorch

Experience in all the following areas:

• RAG
• Vector Search
• LLM Applications
• Dify
• Agentic AI frameworks

Knowledge of:

• Data pipelines
• Data transformation
• Feature engineering.

Experience with:

• OCP (Openshift Platform)
• Docker
• Kubernetes
• CI/CD pipelines
• Git

• Experience with financial services, banking, capital markets, or wealth management.
• Experience building recommendation engines or personalization solutions.
• Experience with search, retrieval, and knowledge management platforms.
• Familiarity with MLOps, LLMOps, and AI governance practices.
• Experience working with unstructured document repositories and enterprise knowledge sources.