Agentic AI Engineer
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Key skills for this role
About the Role
Design and implement agentic AI workflows, develop data pipelines, and integrate systems while ensuring quality and compliance in AI operations.
Key Skills for This Role
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Agentic Ai Engineering
- Design and build end-to-end agentic AI workflows using Microsoft AI Foundry and Copilot Studio, including multi-step agent pipelines capable of autonomous decision-making, tool use, and task delegation.
- Architect and deploy multi-agent systems using orchestration frameworks such as LangGraph, AutoGen, Semantic Kernel, and CrewAI, evaluating fit-for-purpose based on use case complexity and enterprise constraints.
- Implement Retrieval-Augmented Generation (RAG) pipelines incorporating vector databases, semantic search, and knowledge graph integrations to ground agent outputs in verified organisational data.
- Develop and maintain a structured prompt library, applying advanced prompting techniques including chain-of-thought, few-shot, self-reflection, and ReAct patterns to optimise agent behaviour across diverse business scenarios.
- Build and integrate Model Context Protocol (MCP) servers to enable agents to interact with internal tools, APIs, databases, and enterprise systems in a controlled and auditable manner.
- Automate end-to-end job functions across business units, including but not limited to procurement workflows, member services interactions, regulatory reporting, HR task management, document processing, and internal knowledge retrieval.
- Develop AI-powered report generation capabilities that allow agents to autonomously retrieve data, structure findings, and produce formatted outputs for business audiences without manual intervention.
- Design agent memory architectures, managing short-term conversational context and long-term persistent memory stores to enable coherent, stateful interactions across sessions.
- Build and maintain agent evaluation frameworks that measure accuracy, reliability, hallucination rates, task completion, and safety compliance, iterating on agent design based on quantitative outcomes.
- Establish guardrails, content filters, and human-in-the-loop checkpoints to ensure responsible and auditable AI agent behaviour in production environments.
- Implement LLMOps practices including model versioning, prompt version control, agent monitoring, cost tracking, and performance observability using tools such as Azure AI Foundry, LangSmith, or equivalent platforms.
- Integrate agents with enterprise systems including SharePoint, Microsoft Teams, Dynamics 365, and third-party APIs to enable seamless automation across operational workflows.
- Contribute cross-platform knowledge by evaluating and piloting emerging agentic AI tools and frameworks from across the market ensuring the organisation adopts best-in-class approaches rather than remaining constrained to a single vendor ecosystem.
Data Engineering
- Develop and maintain scalable data pipelines using Azure Data Factory, Microsoft Fabric, and Synapse Analytics to ensure agentic systems have reliable access to clean, structured, and contextually relevant data.
- Write production-grade Python and PySpark code for data transformation, orchestration, and integration tasks in support of both AI and analytical workloads.
- Implement and manage Power Automate flows to connect agentic AI outputs to downstream business processes, notifications, and approval workflows.
- Build and maintain vector stores and embedding pipelines using Azure AI Search, Cosmos DB, or equivalent technologies to support RAG and semantic retrieval use cases.
- Collaborate with the Data Engineering team to ensure data quality, lineage, and governance standards are upheld across all datasets consumed by AI agents.
- Support the integration of structured and unstructured data sources into agent-accessible knowledge layers, including document repositories, databases, and real-time event streams.
- Required Qualifications and Experience
- Bachelor's degree or higher in Computer Science, Data Engineering, Artificial Intelligence, or a related technical discipline.
- Demonstrable experience building and deploying agentic AI systems in a production environment, including multi-agent orchestration and autonomous task execution.
- Strong proficiency in Python, including object-oriented design, API development, and integration with AI and data libraries.
- Hands-on experience with Microsoft AI Foundry, Azure OpenAI Service, and Copilot Studio.
- Practical knowledge of RAG architectures, vector databases, and semantic search implementations.
- Solid experience with Azure Data Factory, PySpark, and Microsoft Fabric or Synapse Analytics.
- Working knowledge of Power Automate for workflow automation and enterprise system integration.
- Demonstrated ability to design and maintain a structured prompt library with version-controlled, tested prompt artefacts.
- Familiarity with at least one leading agentic framework beyond the Microsoft stack, such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel.
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