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.