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AI Consultant
- Aggregated by CV-Library
- Sheffield, South Yorkshire
- Permanent
- Full Time
Job Description
AI Engineer
Sheffield
Permanent
The Role
As an AI Engineer, you will be responsible for the design, implementation, and ongoing management of AI platforms, intelligent agents, and supporting infrastructure. You'll combine hands-on technical delivery with strategic input, ensuring AI solutions are scalable, secure, and aligned with organisational objectives.
Key Responsibilities
AI Platform & Infrastructure
Design, implement, and maintain enterprise AI infrastructure across cloud and on-premise environments.
Develop scalable architectures for AI workloads, including model hosting, vector databases, orchestration layers, APIs, and data pipelines.
Integrate AI platforms with business-critical systems, data platforms, collaboration tools, and operational applications.
Produce and maintain architecture documentation, operational procedures, and technical standards.AI Agents & Automation
Design, develop, and manage AI agents, copilots, and workflow automation solutions.
Build Retrieval-Augmented Generation (RAG) applications leveraging internal business data.
Develop intelligent assistants supporting operational, engineering, customer service, and business functions.
Implement multi-agent workflows and orchestration frameworks.
Monitor and optimise AI solution performance, reliability, security, and cost efficiency.Governance & Compliance
Ensure AI solutions align with security, governance, and compliance requirements.
Support data governance, privacy, risk management, and lifecycle policies for AI-enabled systems.
Maintain standards and documentation relating to AI usage, access control, and governance frameworks.
Contribute to responsible AI practices and organisational AI governance initiatives.Stakeholder Engagement
Partner with teams across the organisation to identify and prioritise AI opportunities.
Support user adoption, training, and change management activities.
Translate complex technical concepts into clear business language for non-technical stakeholders.
Act as a trusted advisor on AI technologies and best practices.About You
Essential Skills & Experience
Proven experience designing, building, and deploying AI/ML solutions within enterprise environments.
Strong Python development skills.
Experience integrating and consuming REST APIs.
Hands-on experience with Docker and Kubernetes.
Knowledge of vector databases, embeddings, RAG architectures, and LLM orchestration frameworks.
Experience with cloud technologies, networking, identity and access management, and security controls.
Experience integrating AI solutions with Microsoft 365 technologies, collaboration platforms, and SQL-based databases.
Strong experience working within AWS environments and services.Desirable Experience
AI governance and responsible AI frameworks.
MLOps and LLMOps tools and methodologies.
Prompt engineering and model fine-tuning.
Agentic AI frameworks such as LangGraph, AutoGen, or CrewAI.
AI orchestration platforms such as n8n or similar technologies.
Knowledge graph technologies.
Data lake and analytics platforms.
Experience supporting organisation-wide AI adoption programmes
Sheffield
Permanent
The Role
As an AI Engineer, you will be responsible for the design, implementation, and ongoing management of AI platforms, intelligent agents, and supporting infrastructure. You'll combine hands-on technical delivery with strategic input, ensuring AI solutions are scalable, secure, and aligned with organisational objectives.
Key Responsibilities
AI Platform & Infrastructure
Design, implement, and maintain enterprise AI infrastructure across cloud and on-premise environments.
Develop scalable architectures for AI workloads, including model hosting, vector databases, orchestration layers, APIs, and data pipelines.
Integrate AI platforms with business-critical systems, data platforms, collaboration tools, and operational applications.
Produce and maintain architecture documentation, operational procedures, and technical standards.AI Agents & Automation
Design, develop, and manage AI agents, copilots, and workflow automation solutions.
Build Retrieval-Augmented Generation (RAG) applications leveraging internal business data.
Develop intelligent assistants supporting operational, engineering, customer service, and business functions.
Implement multi-agent workflows and orchestration frameworks.
Monitor and optimise AI solution performance, reliability, security, and cost efficiency.Governance & Compliance
Ensure AI solutions align with security, governance, and compliance requirements.
Support data governance, privacy, risk management, and lifecycle policies for AI-enabled systems.
Maintain standards and documentation relating to AI usage, access control, and governance frameworks.
Contribute to responsible AI practices and organisational AI governance initiatives.Stakeholder Engagement
Partner with teams across the organisation to identify and prioritise AI opportunities.
Support user adoption, training, and change management activities.
Translate complex technical concepts into clear business language for non-technical stakeholders.
Act as a trusted advisor on AI technologies and best practices.About You
Essential Skills & Experience
Proven experience designing, building, and deploying AI/ML solutions within enterprise environments.
Strong Python development skills.
Experience integrating and consuming REST APIs.
Hands-on experience with Docker and Kubernetes.
Knowledge of vector databases, embeddings, RAG architectures, and LLM orchestration frameworks.
Experience with cloud technologies, networking, identity and access management, and security controls.
Experience integrating AI solutions with Microsoft 365 technologies, collaboration platforms, and SQL-based databases.
Strong experience working within AWS environments and services.Desirable Experience
AI governance and responsible AI frameworks.
MLOps and LLMOps tools and methodologies.
Prompt engineering and model fine-tuning.
Agentic AI frameworks such as LangGraph, AutoGen, or CrewAI.
AI orchestration platforms such as n8n or similar technologies.
Knowledge graph technologies.
Data lake and analytics platforms.
Experience supporting organisation-wide AI adoption programmes