Agentic AI Consultant

Responsibilities:

• Design and develop RAG pipelines, agentic workflows, and multi-model orchestration that solve high-value, domain-specific use cases across lines of business.
• Build and optimize the APIs, services, and integration layers that connect AI capabilities to enterprise data and systems.
• Partner with US-based FDEs to turn prioritised, ROI-driven opportunities into production-ready builds. Implement agentic AI systems that improve reasoning, tool use, and interaction across complex, multi-step workflows, working within defined architectural patterns and reference implementations.
• Contribute to and utilise CI/CD pipelines (GitLab or equivalent) to enable automated build, test, and deployment workflows.
• Implement instrumentation, structured logging, and tracing to monitor AI system performance, cost, and output quality, in partnership with platform and SRE teams.
• Leverage AI-assisted development tools to improve code quality, accelerate development, and enhance debugging.
• Contribute design input within defined architectural frameworks and evaluate trade-offs at the component or feature level.
• Collaborate with product, architecture, and engineering partners to translate business requirements into scalable technical solutions.
• Apply and help uphold AI engineering standards for performance, security, reliability, and compliance across deployed solutions.

Requirements:

• 8+ years of professional software development experience, including hands-on experience building AI/ML or LLM-based systems.
• Experience designing and building RAG pipelines, agentic workflows, or multi-model orchestration.
• Experience developing backend services, REST APIs, and enterprise integration solutions using modern languages and frameworks.
• Experience with cloud platforms (AWS/Azure/GCP) and cloud-native application development.
• Experience integrating LLMs and agent frameworks with enterprise data and tooling, including connector/tool-use patterns (e. g., MCP) and retrieval and grounding strategies.
• Experience building and operating CI/CD pipelines (GitLab or equivalent).
• Experience incorporating observability into software solutions, including monitoring of AI system performance, cost, and output quality.
• Understanding of secure application design (authentication/authorization patterns, secrets handling).
• Ability to operate effectively in environments with evolving requirements.
• Strong ownership mindset and clear technical communication skills.
• Experience using AI-assisted development tools to improve productivity and engineering outcomes.

Must-Have Skills:

• Hands-on experience building AI systems using LLMs, RAG, agentic workflows, or multi-model orchestration.
• Experience with cloud platforms (AWS/Azure/GCP) and cloud-native development.
• Experience developing backend services, REST APIs, and integration layers that connect AI capabilities to enterprise systems.
• Experience with agent and tool-use frameworks, including connector patterns (e. g., MCP).
• Experience designing high-throughput, low-latency distributed systems.
• Experience with prompt engineering and LLM evaluation frameworks.