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.
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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 ot
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