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Engineering Tech Lead - Agentic AI and LLM

Primusglobal — United Kingdom · Posted ~3 hours ago

Lead Full-time

Skills

Java Spring Boot REST APIs React PostgreSQL Redis Kafka Prompt Engineering Agentic AI Multi-Agent Systems LLM Evaluation Kubernetes CI/CD Terraform Cloud platforms AWS Azure GCP

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Summary ✨ AI‑Generated

A technology team is seeking an engineering lead with expertise in full-stack systems and production AI applications. The role involves building intelligent services, autonomous workflows, cloud infrastructure, and scalable platforms.

Highlights

Build advanced AI services and scalable engineering platforms while leading full-stack development and production AI workflows.

Description

Role Overview We are looking for an Engineering Tech Lead with strong full-stack engineering and hands-on Agentic AI / LLM experience to build production-grade AI services, agentic workflows and scalable engineering platforms. Must-Have Skills Full-Stack Engineering • Java / Spring Boot • REST APIs & Event-Driven Services • React & API Integration • PostgreSQL & Redis • Kafka / Messaging & Streaming Agentic AI / LLM • Prompt Engineering for Production LLM Applications • Agentic AI / Autonomous Agents • Multi-Agent Systems & Tool-Use Workflows • Planning Loops & Memory-Augmented Reasoning • Structured JSON / Function Calling • Multi-Turn Conversations • LLM Evaluation & Observability • Guardrails & Output Validation • LangSmith / PromptFlow / Braintrust or Custom Evaluation Pipelines DevOps / Cloud • Kubernetes – Deployments, Services, Ingress, Helm, Kustomize, HPA, RBAC • CI/CD – GitHub Actions / GitLab CI / Jenkins • Terraform / Infrastructure as Code • AWS / Azure / GCP Key Responsibilities • Design, build and deploy production-quality services. • Lead development of Agentic AI and LLM-powered systems. • Design and optimise prompts for reasoning, classification, summarisation, code generation and structured outputs. • Build and manage prompt libraries and versioning strategies. • Develop multi-agent and tool-use workflows. • Implement LLM observability, tracing, evaluation and guardrails. • Optimise CI/CD and platform observability. • Work with product and domain teams to translate business requirements into AI solutions. • Support responsible AI deployment, human-in-the-loop workflows and risk mitigation.