Job Description
We are seeking a motivated Decision Science Associate to support the development of Generative AI, analytics, and AI-powered solutions for our clients. Working closely with experienced practitioners, the Associate will contribute to AI application development, knowledge retrieval, analytics, testing, and solution delivery while building expertise in emerging AI technologies.
- Key Responsibilities
- Generative AI, Agentic AI and RAG
Support the development of LLM-powered applications using prompts, APIs, and established frameworks.
Contribute to retrieval-augmented generation (RAG) solutions by preparing, organizing, and validating knowledge sources.
Assist in building and testing conversational AI applications and agent workflows under guidance from senior team members.
Perform functional testing and validation of AI application outputs using defined test cases.
Help analyze responses, identify improvement opportunities, and document findings.
- Cloud Platform Expertise
Work with Azure and/or AWS AI services under supervision.
Support environment configuration, application testing, and monitoring activities.
Learn and apply enterprise security and access management standards.
- AI Engineering and Deployment
Develop Python-based components and integrations with APIs and enterprise systems.
Use Git and standard development practices for source code management and collaboration.
Create technical documentation and support testing, troubleshooting, and deployment activities.
- Required Qualifications : Bachelor’s or Master’s degree in a relevant quantitative or engineering discipline.
- Technical Skills :
- Programming and APIs: Python, SQL, JSON, REST APIs and sound software engineering practices.
- GenAI Fundamentals: Prompt engineering, LLM applications, and basic understanding of RAG and AI assistants.
- Cloud and AI Platforms: Exposure to Azure and/or AWS cloud services.
- Preferred Skills :
Exposure to Generative AI frameworks such as LangChain, LangGraph, or similar tools.
Basic understanding of agentic AI concepts, tool calling, and workflow orchestration.
Knowledge of machine learning libraries such as scikit-learn, TensorFlow, or PyTorch.
Familiarity with vector databases, embeddings, and enterprise knowledge retrieval concepts.
Exposure to Git, Docker, CI/CD, or software development lifecycle practices.
Strong communication, analytical thinking, and collaborative problem-solving skills.
