Job Description

Job Summary

• Develop pipelines to parse documents, chunk, vectorise, and store vector data.
• Design schemas for vector stores to optimise retrieval efficiency.
• Create prompt templates using advanced prompt engineering techniques.
• Implement few-shot prompting strategies for improved LLM interactions.
• Develop semantic agents and interfaces for seamless agent collaboration.
• Build microservices and expose them as APIs for scalable solutions.
• Work closely with architects and full-stack developers to deliver AI solutions.
• Apply best practices for data management and model deployment.
• Ensure scalability and performance of generative AI applications.

Must-Have Skills

• Designed and developed scalable generative AI platforms for enterprise use cases.
• Built end-to-end pipelines for document parsing, chunking, vectorisation, and storage in vector databases like FAISS, Pinecone, and Chroma.
• Created and optimised vector store schemas for efficient semantic retrieval.
• Developed prompt templates using few-shot and zero-shot prompting techniques.
• Engineered semantic agents and built interfaces for agent collaboration in LLM workflows.
• Built and deployed microservices exposing LLM capabilities through scalable APIs.

Job Highlights

Qualifications

• Designed and developed scalable generative AI platforms for enterprise use cases
• Built end-to-end pipelines for document parsing, chunking, vectorisation, and storage in vector databases like FAISS, Pinecone, and Chroma
• Created and optimised vector store schemas for efficient semantic retrieval
• Engineered semantic agents and built interfaces for agent collaboration in LLM workflows
• Built and deployed microservices exposing LLM capabilities through scalable APIs

Responsibilities

• Develop pipelines to parse documents, chunk, vectorise, and store vector data
• Design schemas for vector stores to optimise retrieval efficiency
• Create prompt templates using advanced prompt engineering techniques
• Implement few-shot prompting strategies for improved LLM interactions
• Develop semantic agents and interfaces for seamless agent collaboration
• Build microservices and expose them as APIs for scalable solutions
• Work closely with architects and full-stack developers to deliver AI solutions
• Apply best practices for data management and model deployment
• Ensure scalability and performance of generative AI applications
• Developed prompt templates using few-shot and zero-shot prompting techniques

Job Highlights

Key Qualifications

• Experience designing scalable generative AI platforms for enterprise use.
• Proven track record building end-to-end pipelines for document parsing, chunking, vectorisation, and storage in vector databases such as FAISS, Pinecone, and Chroma.
• Skilled in creating and optimising vector store schemas for effective semantic retrieval.
• Expertise in developing prompt templates utilizing few-shot and zero-shot prompting techniques.
• Knowledge in engineering semantic agents and creating interfaces for collaboration within LLM workflows.
• Experience building and deploying microservices that expose LLM capabilities via APIs.

Core Responsibilities

• Develop and maintain pipelines for document processing and vector data management.
• Design efficient schemas for vector stores.
• Create advanced prompt templates.
• Implement prompting strategies to enhance LLM interactions.
• Develop semantic agents and collaborative interfaces.
• Build microservices to serve AI functionalities.
• Collaborate with cross-functional teams for AI solution delivery.
• Follow best practices for data and model deployment.
• Ensure the scalability and performance of AI applications.