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.