Key Responsibilities
- Design and build agentic LLM solutions (single- and multi-agent patterns) to solve real business problems across domains (e.g., customer support, document intelligence, knowledge retrieval).
- Build RAG pipelines end-to-end: data ingestion → chunking/embeddings → vector search → retrieval orchestration → response synthesis, with measurable quality.
- Implement prompt engineering and prompt orchestration (prompt chains, tool-calling, function calling), including prompt iteration and cost/latency optimisation.
- Develop production services/APIs for LLM applications (e.g., FastAPI/Flask/Streamlit) and integrate with enterprise systems and data sources.
- Apply guardrails to reduce hallucinations, enforce policy constraints, and ensure safe tool usage; implement evaluation strategies for LLM and RAG outputs.
- Collaborate with Data Engineering teams to ensure data quality, governance, and documentation standards, and with MLOps/Platform teams for CI/CD, monitoring, and reliable deployments.
- Create and maintain technical documentation, solution design artefacts, and reusable components for faster delivery and consistent engineering practices.
Must-Have Skills
5 to 12 years total experience, with hands-on LLM/GenAI delivery experience (preferably 1–3+ years building production-grade LLM apps).
LLM / GenAI & Agentic Engineering
- Hands-on experience with LLMs including Claude (Anthropic) and other leading models; strong understanding of capabilities, limitations, and use-case fit.
- Practical experience with RAG, embeddings, vector databases (e.g., FAISS/Pinecone/ChromaDB), semantic search, and retrieval quality evaluation.
- Experience with frameworks/tools such as LangChain, LangGraph, Hugging Face, or equivalent orchestration stacks.
- Experience building agentic workflows including tool calling/function calling; familiarity with “agentic architecture” concepts is valued.
- Exposure to Claude Code or similar coding-agent workflows is a plus (agentic coding that can work across codebases, run tests, and iterate).
Core Engineering
- Strong Python engineering skills (production-grade coding, testing, packaging, API development).
- Solid understanding of cloud platforms (Azure/AWS/GCP) and deployment basics (containers, CI/CD, monitoring).
- Strong communication skills—ability to translate business needs into technical solutions and articulate trade-offs clearly.
Mandatory Background (Non-negotiable)
- Prior experience in Data Engineering or Data Science:
- Data pipelines / ETL / ELT / orchestration, or
- ML/NLP modelling lifecycle, experimentation, evaluation, or
- Analytics engineering and data product delivery.
Good-to-Have / Preferred
- Fine-tuning approaches (e.g., LoRA/PEFT), prompt tuning, few-shot strategies, and model evaluation methods.
- Experience with enterprise-grade privacy/security considerations for GenAI solutions (data handling, redaction, access control).
- Experience with Azure stack components often used in GenAI (e.g., Azure AI Search / Azure OpenAI) is beneficial.
Education
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Information Systems, or related fields (or equivalent practical experience).
Key Responsibilities
- Design and build agentic LLM solutions (single- and multi-agent patterns) to solve real business problems across domains (e.g., customer support, document intelligence, knowledge retrieval).
- Build RAG pipelines end-to-end: data ingestion → chunking/embeddings → vector search → retrieval orchestration → response synthesis, with measurable quality.
- Implement prompt engineering and prompt orchestration (prompt chains, tool-calling, function calling), including prompt iteration and cost/latency optimisation.
- Develop production services/APIs for LLM applications (e.g., FastAPI/Flask/Streamlit) and integrate with enterprise systems and data sources.
- Apply guardrails to reduce hallucinations, enforce policy constraints, and ensure safe tool usage; implement evaluation strategies for LLM and RAG outputs.
- Collaborate with Data Engineering teams to ensure data quality, governance, and documentation standards, and with MLOps/Platform teams for CI/CD, monitoring, and reliable deployments.
- Create and maintain technical documentation, solution design artefacts, and reusable components for faster delivery and consistent engineering practices.
Must-Have Skills
5 to 12 years total experience, with hands-on LLM/GenAI delivery experience (preferably 1–3+ years building production-grade LLM apps).
LLM / GenAI & Agentic Engineering
- Hands-on experience with LLMs including Claude (Anthropic) and other leading models; strong understanding of capabilities, limitations, and use-case fit.
- Practical experience with RAG, embeddings, vector databases (e.g., FAISS/Pinecone/ChromaDB), semantic search, and retrieval quality evaluation.
- Experience with frameworks/tools such as LangChain, LangGraph, Hugging Face, or equivalent orchestration stacks.
- Experience building agentic workflows including tool calling/function calling; familiarity with “agentic architecture” concepts is valued.
- Exposure to Claude Code or similar coding-agent workflows is a plus (agentic coding that can work across codebases, run tests, and iterate).
Core Engineering
- Strong Python engineering skills (production-grade coding, testing, packaging, API development).
- Solid understanding of cloud platforms (Azure/AWS/GCP) and deployment basics (containers, CI/CD, monitoring).
- Strong communication skills—ability to translate business needs into technical solutions and articulate trade-offs clearly.
Mandatory Background (Non-negotiable)
- Prior experience in Data Engineering or Data Science:
- Data pipelines / ETL / ELT / orchestration, or
- ML/NLP modelling lifecycle, experimentation, evaluation, or
- Analytics engineering and data product delivery.
Good-to-Have / Preferred
- Fine-tuning approaches (e.g., LoRA/PEFT), prompt tuning, few-shot strategies, and model evaluation methods.
- Experience with enterprise-grade privacy/security considerations for GenAI solutions (data handling, redaction, access control).
- Experience with Azure stack components often used in GenAI (e.g., Azure AI Search / Azure OpenAI) is beneficial.
Education
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Information Systems, or related fields (or equivalent practical experience).
Key Responsibilities
- Design and build agentic LLM solutions (single- and multi-agent patterns) to solve real business problems across domains (e.g., customer support, document intelligence, knowledge retrieval).
- Build RAG pipelines end-to-end: data ingestion → chunking/embeddings → vector search → retrieval orchestration → response synthesis, with measurable quality.
- Implement prompt engineering and prompt orchestration (prompt chains, tool-calling, function calling), including prompt iteration and cost/latency optimisation.
- Develop production services/APIs for LLM applications (e.g., FastAPI/Flask/Streamlit) and integrate with enterprise systems and data sources.
- Apply guardrails to reduce hallucinations, enforce policy constraints, and ensure safe tool usage; implement evaluation strategies for LLM and RAG outputs.
- Collaborate with Data Engineering teams to ensure data quality, governance, and documentation standards, and with MLOps/Platform teams for CI/CD, monitoring, and reliable deployments.
- Create and maintain technical documentation, solution design artefacts, and reusable components for faster delivery and consistent engineering practices.
Must-Have Skills
5 to 12 years total experience, with hands-on LLM/GenAI delivery experience (preferably 1–3+ years building production-grade LLM apps).
LLM / GenAI & Agentic Engineering
- Hands-on experience with LLMs including Claude (Anthropic) and other leading models; strong understanding of capabilities, limitations, and use-case fit.
- Practical experience with RAG, embeddings, vector databases (e.g., FAISS/Pinecone/ChromaDB), semantic search, and retrieval quality evaluation.
- Experience with frameworks/tools such as LangChain, LangGraph, Hugging Face, or equivalent orchestration stacks.
- Experience building agentic workflows including tool calling/function calling; familiarity with “agentic architecture” concepts is valued.
- Exposure to Claude Code or similar coding-agent workflows is a plus (agentic coding that can work across codebases, run tests, and iterate).
Core Engineering
- Strong Python engineering skills (production-grade coding, testing, packaging, API development).
- Solid understanding of cloud platforms (Azure/AWS/GCP) and deployment basics (containers, CI/CD, monitoring).
- Strong communication skills—ability to translate business needs into technical solutions and articulate trade-offs clearly.
Mandatory Background (Non-negotiable)
- Prior experience in Data Engineering or Data Science:
- Data pipelines / ETL / ELT / orchestration, or
- ML/NLP modelling lifecycle, experimentation, evaluation, or
- Analytics engineering and data product delivery.
Good-to-Have / Preferred
- Fine-tuning approaches (e.g., LoRA/PEFT), prompt tuning, few-shot strategies, and model evaluation methods.
- Experience with enterprise-grade privacy/security considerations for GenAI solutions (data handling, redaction, access control).
- Experience with Azure stack components often used in GenAI (e.g., Azure AI Search / Azure OpenAI) is beneficial.
Education
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Information Systems, or related fields (or equivalent practical experience).