Agentic/AI engineers with Claude/code/LLM skills1

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).