Junior AI Engineer
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.