AI/ML Engineer (Agentic AI & Data Science)
About the Role
We are seeking an AI/ML Engineer with Data Science and GenerativeAI 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.
Key Responsibilities
Generative AI & Agentic AI
- 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.
Machine Learning & Data Science
- 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.
AI Application Development
- 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.
Required Qualifications
Experience:
- Machine Learning Engineering
- Data Science
- AI Engineering
- Advanced Analytics
- Hands-on experience building and deploying ML or AI solutions into production.
Technical Skills
Programming
- Python (mandatory)
- SQL
- REST APIs
Machine Learning
Experience with:
- Scikit-Learn
- XGBoost/ LightGBM
- TensorFlow or PyTorch
Generative AI – (MANDATORY)
Experience in all the following areas:
- RAG
- Vector Search
- LLM Applications
- Dify
- Agentic AI frameworks
Data Engineering
Knowledge of:
- Data pipelines
- Data transformation
- Feature engineering
- Data quality management
Cloud & DevOps
Experience with:
- OCP (Openshift Platform)
- Docker
- Kubernetes
- CI/CD pipelines
- Git
Preferred Qualifications
- 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.