AI Consultant

What we’re looking for

We’re looking for an AI Consultant who combines strong software engineering capability with a consulting mindset.

You’ll be comfortable working directly with clients on well-defined pieces of work, exploring how their business operates and turning clearly scoped problems into practical technical solutions. You’ll know how to establish the business need before choosing the technology, and take ownership of your workstreams from discovery through to deployment and handover, drawing on senior consultants for the most complex or ambiguous calls.

You are likely to be someone who:

• Starts with the business problem and desired outcome, rather than the technology.
• Moves comfortably between stakeholder conversations and hands-on technical delivery.
• Works through ambiguity and applies structured problem-solving, and knows when to bring in senior support.
• Takes ownership of the quality, expectations and outcomes of the work they run.
• Can communicate the capabilities, limitations and risks of AI clearly.
• Looks for opportunities to improve delivery through reusable tools, automation and better ways of working.

The role

As an AI Consultant, you’ll own defined workstreams within client engagements where LLM-based solutions, automation and systems integration are central to the solution, with senior consultants providing oversight on the broader engagement.

You’ll discover and map business processes, translate client needs into technical requirements, and design and build production-ready LLM-powered workflows, agents, RAG applications and automations for your workstreams. This includes building MCP servers and custom skills and extensions for AI assistants such as Claude, Copilot and ChatGPT. You’ll stay involved through integration, testing, deployment, client acceptance and handover.

This is a hands-on role that combines software engineering, AI delivery and client consulting. You’ll use AI-assisted development and agentic workflows pragmatically to improve delivery speed and quality, while keeping solutions secure, reliable and maintainable.

Key responsibilities

• Take part in stakeholder interviews and workshops for your workstreams, walking through client processes and always asking why before how.
• Translate business needs and workflows into clear solution designs and engineering requirements.
• Design, develop and integrate production-ready LLM workflows, agents, RAG pipelines, automations and supporting applications.
• Build solutions on cloud AI platforms such as Azure AI Foundry, AWS Bedrock or GCP Vertex AI, integrating with applications, APIs, databases and cloud environments.
• Build MCP servers, skills and extensions that connect AI assistants such as Claude and ChatGPT to client systems and workflows.
• Develop maintainable, secure and well-tested Python and SQL solutions.
• Design and run evaluations to assess the accuracy, reliability and behaviour of LLM-based solutions.
• Implement appropriate observability , monitoring and alerting, and improve solutions based on production performance.
• Diagnose and resolve issues across application, data and infrastructure layers, escalating complex or high-risk issues to senior consultants.
• Support solution demonstrations, client onboarding, acceptance testing and technical handover.
• Communicate effectively with technical and non-technical stakeholders throughout the engagement.
• Build relevant industry and business-process knowledge, and apply it across engagements.
• Create reusable agents, templates, automation pipelines and playbooks.

Essential skills and experience

• Three to five years of experience in software engineering, solutions engineering, AI engineering or a related technical role.
• Experience delivering technical solutions in a client-facing or production environment.
• Strong Python and SQL skills, including the ability to write professional, maintainable and production-ready code.
• Hands-on experience building, testing, evaluating and operating LLM-powered solutions, such as RAG applications, AI agents and intelligent automations.
• Strong prompt and context engineering skills, and practical approaches to evaluating LLM behaviour, accuracy and reliability.
• Experience with cloud AI services such as Azure AI Foundry, Azure OpenAI or equivalent AWS/GCP AI platforms.
• Experience working with APIs, relational databases, data models and systems integrations.
• Experience working in cloud environments (ideally Azure) and with CI/CD pipelines and automated deployments.
• Understanding of secure software development practices, including authentication, authorisation, API security and secrets handling.
• Experience using Git workflows, pull requests and code reviews.
• Strong troubleshooting, critical-thinking and structured problem-solving skills.
• Strong client communication and stakeholder-management skills.

Desirable experience

• Experience working in professional consulting or delivering software solutions across multiple client environments.
• Experience building MCP servers, or skills, plugins or extensions for AI assistants such as Claude or ChatGPT.
• Familiarity with AI orchestration frameworks and agent harnesses.
• Working knowledge of containers and observability practices, including logging, monitoring and alerting.
• Understanding of dependency management, encryption and core networking concepts.
• Experience with JavaScript and front-end frameworks such as React.
• Experience building client-facing interfaces or full-stack solutions.
• Experience using AI-assisted development tools (such as GitHub Copilot, Claude Code or Cursor) and agentic coding workflows in day-to-day delivery.
• Experience working with agile delivery tools such as Jira or ClickUp.

What success looks like

• Delivering reliable AI and automation solutions for your workstreams that create measurable value for clients.
• Earning client trust through strong discovery, clear communication and dependable delivery.
• Managing your workstreams effectively from initial scoping through to deployment, acceptance and handover.
• Setting realistic expectations about the behaviour, limitations and risks of AI solutions.
• Using AI-assisted development and automation to improve delivery speed and quality.
• Creating reusable tools and delivery assets that reduce manual effort and improve consistency.
• Applying knowledge from previous engagements to accelerate future discovery and delivery.

About Synogize

At Synogize, we harness synergy and passion to drive success. Founded by Data & Analytics professionals, we pride ourselves on bridging the gap between people, processes, and technology to deliver innovative solutions. Our mission is to create transformative outcomes by aligning data, technology, and talent, helping organizations shape the future of innovation.