Job at a glance
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: o Client propensity prediction o Recommendation systems o Classification and ranking o Behavioral analytics o 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 • Experience in: o Machine Learning Engineering o Data Science o AI Engineering o 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.