Agentic/AI engineers with Claude/code/LLM skills1

Job description

**Job Description: 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). **Responsibilities: 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** :