Job Description
Job Responsibilities
- Design and implement generative AI (GenAI) pipelines leveraging large language models (LLMs) with SQL, vector-based, and semantic data inputs.
- Develop robust RESTful APIs to support chatbot communication, analytics, and internal tooling.
- Integrate and fine-tune semantic search tools (e.g., Weaviate), alongside structured database systems such as SQL and PostgreSQL.
- Build dynamic and adaptive prompt templates that incorporate memory functions, tool syntax, and schema-based grounding.
- Architect intelligent agent workflows capable of processing graph-based knowledge and contextual reasoning.
- Maintain user-friendly internal dashboards and interfaces for validation, control, and performance tracking.
- Oversee system performance, including caching, monitoring, and logging using Redis and PostgreSQL.
- Collaborate closely with product and data teams in Agile environments to enhance platform capabilities and user experience.
Requirements
Job Requirements
- Minimum 5 years of full-stack development experience, with at least 1โ2 years focused on GenAI/LLM implementation.
- Advanced proficiency in Python, particularly with backend frameworks such as Flask or FastAPI.
- Demonstrated expertise in designing and deploying RESTful APIs.
- Experience working with LLMs via API access (e.g., OpenAI, Gemini, Claude).
- Familiarity with prompt engineering principles, token optimisation, and few-shot training approaches.
- Strong grasp of semantic search methodologies, embeddings, and vector databases (e.g., Weaviate, Pinecone).
- Competence in SQL query construction and validating AI-generated database syntax.
- Solid knowledge of frontend technologies, including HTML, JavaScript, AJAX, and templating engines like Jinja2.
- Hands-on experience with Redis, PostgreSQL, and data formats such as JSON.
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