Agentic AI Engineer: Cloud-Native & Enterprise Apps

Job Title: Agentic AI Software Engineer

Location: Willing to travel to client sites throughout the UK on an ad hoc basis.

Salary: Competitive salary and package (Depending on level of experience).

Qualifications

• You will bring 2 years of experience building cloud-native solutions and orchestrating cross‑functional initiatives.
• You combine hands‑on engineering depth with the creativity and product intuition needed to shape solutions that work for real users and enterprises.
• You’re as comfortable writing production code as you are whiteboarding system designs, facilitating client workshops, or mapping out product roadmaps.
• You thrive in ambiguity and act as a bridge between vision and execution.

Responsibilities

• Product Orchestration: Partner with business and technical stakeholders to frame problems, align priorities, and translate needs into orchestrated AI workflows.
• Agent Architecture & Engineering: Design and engineer enterprise‑ready AI agents—including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Creative Problem Solving: Prototype novel solutions that blend technical rigor with human-centered product thinking.
• AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and enablement.
• Cloud-Native Product & Software Engineering: Apply containerization (Kubernetes, Docker), microservices, serverless, event‑driven architectures, CI/CD, and observability to create and integrate scalable AI-native systems.
• Domain-Specific Workflows: Tailor and deploy agentic applications across industry verticals (e.g. finance, healthcare, retail)—addressing domain-specific processes via intelligent automation.
• Storytelling & Communication: Lead design workshops, POCs, and code-with sessions; communicate technical tradeoffs and solution impact clearly to both executives and engineers.
• Measure & Improve: Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
• Knowledge Sharing: Craft reusable patterns, documentation, and best practices to influence internal assets and client roadmaps.

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