About the role
structured by ORIKey Responsibilities Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence). Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.
What you will do
- Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence).
- Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.
- Implement prompt engineering techniques (prompt design, chaining, optimisation).
- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit).
- Integrate LLM solutions with enterprise systems and structured/unstructured data sources.
What they are looking for
- 2–4 years total experience, with exposure to AI/ML, NLP, or Data Engineering projects
- Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)
- Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation
- GPT + Agentic AI implementation experience
- Experience with: - LangChain, LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Deep understanding of: - LLM limitations, evaluation, and optimisation strategies
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to: - Cloud platforms (Azure/AWS/GCP)
Nice to have
- Exposure to agentic workflows or tool calling concepts
- Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)
- Experience with Azure OpenAI / Azure AI Search or similar stacks
- Awareness of enterprise AI considerations (data security, privacy, governance)
Full posting text
Key Responsibilities
Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence).
Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval.
Implement prompt engineering techniques (prompt design, chaining, optimisation).
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit).
Integrate LLM solutions with enterprise systems and structured/unstructured data sources.
Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations.
Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness.
Document solutions and contribute to reusable components and best practices.
Must-Have Skills
Experience
2–4 years total experience, with exposure to AI/ML, NLP, or Data Engineering projects
Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)
LLM / GenAI & Agentic Engineering
Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.)
RAG pipelines and retrieval optimisation
GPT + Agentic AI implementation experience
Experience with: - LangChain, LangGraph, or similar frameworks
Agent orchestration and tool-calling architectures
Deep understanding of: - LLM limitations, evaluation, and optimisation strategies
Core Engineering
Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
Deep data analysis experience and handling large volume of data
Fabric/Azure Databricks/Snowflake data engineering integration skills
Good exposure to: - Cloud platforms (Azure/AWS/GCP)
SQL
Containers, CI/CD, monitoring
Data / AI Foundations (Mandatory)
Prior Experience In One Or More
Data Engineering (ETL/ELT, pipelines, orchestration)
Data Science / ML lifecycle (especially NLP)
Analytics engineering / data products
Good-to-Have
Exposure to agentic workflows or tool calling concepts
Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)
Experience with Azure OpenAI / Azure AI Search or similar stacks
Awareness of enterprise AI considerations (data security, privacy, governance)
Employment type: Full-time
Industries: Business Consulting and Services