Job Summary

This role is accountable for delivering advanced Generative AI solutions by leveraging expertise in Large Language Models (LLMs), prompt engineering, and modern Python frameworks. The individual applies advanced proficiency in designing, developing, and optimizing AI-driven applications, ensuring technical excellence and strategic alignment with project goals. They guide teams in implementing robust architectures and best practices, contributing to the successful execution of complex development projects.

Key Responsibilities

1. Lead solution development using GenAI, LLMs, and prompt engineering to architect and implement scalable AI applications, ensuring alignment with project requirements.
2. Develop and optimize RESTful APIs with Python frameworks such as Flask, Django, and FastAPI to enable seamless integration of AI models and data sources.
3. Design and maintain data pipelines utilizing MySQL, PostgreSQL, and VectorDB technologies (e.g., Pinecone), ensuring efficient storage and retrieval of embeddings and model outputs.
4. Implement advanced prompt engineering techniques to enhance LLM performance, tailoring prompts for specific use cases and optimizing model responses.
5. Oversee code quality and performance by conducting comprehensive code reviews and enforcing best practices in Python and AI model development.
6. Integrate Azure AI Search and other cloud-based tools to support retrieval-augmented generation and scalable deployment of GenAI solutions.
7. Collaborate within the development team to resolve technical challenges, provide guidance on architectural decisions, and ensure process compliance throughout the project lifecycle.

Skill Requirements

AI Solutions Expert Vacancy Role purpose We are looking for an AI Solutions Expert to help build practical AI solutions within the Finance & Risk Grid. The role will focus on turning identified use cases into working solutions that improve productivity, analysis, validation, migration, and operational processes across the grid. The expert will work closely with business and IT SPOCs across FR grid, helping teams move from ideas to implementation and establishing reusable patterns for AI adoption within the grid. Key responsibilities Translate business and IT AI use cases into practical solution designs and prototypes Build AI-enabled solutions for areas such as: document and knowledge intelligence data query / validation / comparison support migration and code transformation support regression and reconciliation support production and operational intelligence Work with business and IT SPOCs to understand processes, pain points, and expected outcomes Design and develop AI-assisted solutions using approved tools and platforms Build reusable components and patterns that can scale across projects in the Finance & Risk Grid Support rapid pilots, proofs of concept, and structured scaling of successful solutions Help define guardrails, quality checks, and practical governance for AI usage Contribute to skill build-up within the grid by coaching teams and sharing implementation knowledge Work closely with architects, platform teams, and AI COE where needed Required profile Strong hands-on experience in building AI-enabled applications or solutions Good practical knowledge of LLM-based application patterns such as: prompt design structured outputs RAG semantic search / vector search Strong Python development skills Good experience with SQL and data analysis / comparison logic Experience in building APIs, services, or backend components for AI solutions Ability to convert use cases into practical implementation steps Strong communication skills and ability to work with both technical and non-technical stakeholders Ability to work in an environment where business context, data, and governance matter Preferred / desirable experience Experience with GitHub Copilot / developer productivity tooling Experience with document-heavy enterprise AI use cases Experience in migration / transformation support, including code conversion or code understanding use cases Experience in regulated environments such as banking, risk, finance, or compliance Familiarity with production support, validation, reconciliation, or control processes Experience in setting up evaluation and monitoring for AI solutions Technical skills Must-have Python SQL REST APIs JSON / structured data handling AI application development using LLMs Prompt engineering for enterprise use cases RAG / vector-search-based solution patterns Nice-to-have Databricks Azure OpenAI / Azure AI services FastAPI or Flask GitHub Copilot YAML DevOps / CI-CD exposure basic frontend integration understanding Ways of working Works in close collaboration with grid AI lead, architects, and business/IT SPOCs Helps structure work into quick wi

Other Requirements

1. Optional but valuable:
2. Microsoft Certified: Azure AI Engineer Associate
3. - Google Professional Machine Learning Enginee