Senior AI Engineer

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 optimise 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).
Nice-to-Have:
Experience designing high-throughput, low-latency distributed systems.
Experience with prompt engineering and LLM evaluation frameworks.
Experience with AI observability output evaluation, hallucination detection, or agent tracing.
Familiarity with frontend technologies (e. g., React) for building end-user AI experiences.
Experience leveraging AI for log analysis, anomaly detection, or operational insights.
Experience working in a forward-deployed, consulting, or customer-facing engineering model.
Experience delivering solutions across multiple lines of business or domains, matching engineering effort to business value.

Experience
6-10 yrs