We are seeking an AI Solutions Engineer, Agentic AI to design, build and scale practical AI agent solutions for enterprise users. This role is ideal for a hands-on engineer with experience developing LLM applications, Retrieval-Augmented Generation pipelines, prompt workflows and applied machine learning capabilities that can be deployed in real-world environments.

You will help transform complex, knowledge-intensive workflows into trusted AI-powered experiences. The ideal candidate combines strong software engineering fundamentals with practical AI delivery experience, and is motivated by building systems that are accurate, secure, explainable, reusable and easy for users to adopt.

Key Responsibilities

1. Build AI Agent Workflows

Design and develop AI agent workflows that support enterprise users across a range of knowledge-intensive tasks.
Build reasoning logic that helps agents understand user intent, retrieve relevant context, generate draft outputs and route tasks appropriately.
Translate complex user journeys, operational processes and information flows into scalable agent designs.
Implement prompt chains, tool-calling flows, decision logic and structured outputs.
Ensure agent behavior is reliable, auditable and aligned with user needs, security expectations and quality standards.

2. Build RAG Pipelines and Knowledge Systems

Design and maintain Retrieval-Augmented Generation pipelines that connect AI agents to approved knowledge sources.
Ingest, process and structure documents, reference materials and datasets for accurate search and retrieval.
Apply chunking, embeddings, tagging and indexing methods to improve search and retrieval quality.
Curate knowledge bases so outdated, duplicated or low-quality content does not affect agent outputs.
Implement citations, confidence scoring and retrieval evaluation to help users trace answers back to trusted sources.

3. Develop Applied ML and Predictive Capabilities

Build and evaluate ML models for use cases where predictive or analytical techniques are required alongside generative AI.
Develop predictive models using appropriate time-series, regression, ensemble or ML techniques.
Use approved structured and unstructured data signals as model features while applying appropriate controls for sensitive information.
Compare model outputs against actual outcomes and agreed assumptions to improve accuracy over time.
Collaborate with subject matter experts to validate assumptions, interpret results and improve model performance.

4. Improve Prompts, Models and Output Quality

Design, test and optimise prompts for different agent use cases.
Create reusable prompt templates and system instructions for summarisation, drafting, analysis, benchmarking and recommendations.
Build evaluation datasets and test cases to measure factual accuracy, retrieval quality, consistency, hallucination risk, tone and completeness.
Fine-tune or adapt models where needed to improve domain-specific performance.
Implement guardrails to reduce unsupported claims, poor-quality recommendations and unsafe outputs.

5. Integrate Agents with Enterprise Systems

Build APIs, connectors and function-calling interfaces so agents can retrieve data and trigger downstream workflows.
Integrate agents with document repositories, approved data sources, internal tools and knowledge platforms.
Develop controlled tool-calling patterns that allow agents to interact safely with approved systems.
Partner with engineering teams to deploy integrations securely and reliably.

6. Support Knowledge Governance and Continuous Improvement

Set up processes to capture SME corrections, user feedback and approved responses into reusable knowledge.
Build feedback loops so agents improve over time without weakening governance or quality controls.
Support human-in-the-loop review for high-value or high-risk outputs.
Maintain versioning of prompts, knowledge bases, model configurations and evaluation outputs.
Ensure agent outputs can be reviewed, traced and improved based on operational feedback.

7. Collaborate Across Product, Engineering and Governance

Work with product, engineering, governance and operations stakeholders to understand requirements and validate outputs.
Partner with product owners and experience designers to translate user needs into practical agent capabilities.
Partner with platform engineers to ensure models, pipelines and agent services run reliably on approved infrastructure.
Explain technical trade-offs clearly to non-technical stakeholders.
Support demos, pilots, user acceptance testing and production rollout activities.

Hiring Companies

Filter

SAP Project Systems & Plant Maintenance Lead

Business Analyst

Engineer II, IT Infrastructure (Cloud Engineer)

Assistant Solution Manager (cloud)

GPUaaS Operation Engineer, Data Center Operations

AI Solutions Engineer, Agentic AI

Senior AI Native Engineer (Software Engineering)

Cybersecurity Engineer

Senior Executive, Project Management and Transformation

Software Engineering Intern