Purpose of the Role

The Data Scientist contributes to the design, development, and deployment of generative AI and AI agent solutions within the AI Center of Excellence. The role owns key components of end-to-end GenAI projects, including data preparation, experimentation, model testing, and deployment support, while working collaboratively with senior members of the team.

This position applies working expertise in large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and agentic AI systems to help the team solve business problems and automate processes. The Data Scientist supports AI initiatives aligned with business impact and the organization's broader transformation goals, working closely with the Head of the AI Center of Excellence and other team members.

Key Responsibilities

GenAI Solution Development

• Contribute to developing GenAI infrastructure components, including LLM deployment pipelines, vector databases, API integrations, and agent orchestration frameworks for assigned projects.

• Apply prompt optimization and model selection techniques to support the quality, cost-efficiency, and latency of generative AI solutions.

• Support the security, reliability, and scalability of deployed AI systems, including monitoring for model drift and system issues on assigned projects.

• Develop LLM-powered applications, such as chatbots, document processing systems, content generation tools, and intelligent assistants relevant to financial services.

• Build and maintain RAG pipelines, knowledge bases, and data ingestion workflows to ground generative AI outputs in accurate, domain-specific information.

• Deploy and monitor GenAI solutions using LLMOps best practices, including evaluation frameworks, prompt versioning, and guardrails.

• Use cloud AI platforms and managed LLM services (e.g., Azure OpenAI, AWS Bedrock, GCP Vertex AI) to support the deployment of generative AI solutions.

• Contribute to the development of AI agents and multi-agent systems for task automation, decision support, and workflow orchestration.

Project Participation & Collaboration

• Participate in AI/ML projects from data exploration to deployment, owning technical tasks and timelines in coordination with the Head of the AI Center of Excellence.

• Conduct experimentation and apply relevant GenAI research (e.g., new LLM architectures, reasoning models, multi-modal models, agentic frameworks) to assigned business use cases.

• Prepare documentation and summaries of GenAI capabilities and agent architectures for review by AI Transformation Office leadership and cross-functional teams.

• Follow responsible AI principles and support the implementation of guardrails, content moderation, and bias mitigation strategies for assigned GenAI deployments.

• Collaborate with business, product, and engineering teams to understand GenAI opportunities and contribute to real-world impact.

• Participate in knowledge sharing with peers through code reviews, technical discussions, and best-practice sharing.

• Stay current with GenAI research and industry trends relevant to assigned projects, and apply this knowledge to improve personal contribution and team outcomes.

Key Relationships

• Reports to the Head of the AI Center of Excellence and works closely with other members of the AI Transformation Office team.

• Collaborates with teams across PASHA Financial Holding to align on GenAI initiatives and follow up on project updates.

• May interact with external consultants for research, benchmarking, and process support.

Requirements

Required:

• 3-5 years of relevant work experience, including exposure to data science, machine learning, or AI-related projects.

• Undergraduate degree or higher in a relevant field.

• Proficiency in Azerbaijani and English.

• Working knowledge of Python and related libraries (e.g., LangChain, LlamaIndex, Hugging Face, OpenAI SDK).

• Familiarity with API development (e.g., FastAPI), vector databases (e.g., ElasticSearch, Milvus, Qdrant), cloud AI services (e.g., AWS, GCS), version control (e.g., Git/GitHub), notebook environments (e.g., Jupyter), and shell scripting.

• Understanding of data preprocessing and working with structured and unstructured datasets, including text/document processing and embedding generation.

• Foundational knowledge of large language models (LLMs), prompt engineering, fine-tuning, RAG architectures, multi-modal models, and evaluation frameworks.

Preferred:

• Exposure to LLM-based agents, multi-agent systems, tool use and function calling, or agentic frameworks (e.g., LangChain, LangGraph, PydanticAI).

• Genuine interest in AI research and open-source innovation, with a habit of staying current on developments in the field.