GenAI | LangChain | RAG | AWS | Kubernetes | Client-Facing

Were looking for a Forward Deployed Engineer to drive the implementation and rollout of enterprise-grade Generative AI and LLM solutions within production environments.

This is a hands-on, customer-facing position focused on architecting and deploying RAG pipelines, agent-based systems, and fine-tuned LLM applications using frameworks such as LangChain and LangGraph. You will work closely with client teams, take full ownership of technical delivery from proof of concept through to production, and deliver measurable KPI-driven outcomes.

Key Responsibilities

• Architect and implement GenAI / LLM solutions (RAG, agentic workflows, fine-tuning)
• Lead cloud-based production deployments across AWS, Azure, or GCP
• Develop scalable backend services in Python (JavaScript is advantageous)
• Manage and provision infrastructure using Kubernetes, Docker, and Terraform
• Own end-to-end technical delivery across multiple enterprise engagements
• Perform debugging, optimisation, and root cause analysis
• Clearly communicate complex AI concepts to both technical and non-technical stakeholders

Requirements

• Proven experience building and deploying LLM / Generative AI applications
• Strong hands-on expertise with LangChain, RAG architectures, and multi-agent systems
• Experience deploying ML solutions in cloud environments (AWS, Azure, or GCP)
• Familiarity with DevOps practices and tooling: Kubernetes, CI/CD, GitHub, GitOps
• Prior experience in client-facing technical delivery roles
• Background in Machine Learning, Data Science, or AI Engineering

This opportunity is ideal for a Senior Machine Learning Engineer, Applied AI Engineer, AI Solutions Engineer, or LLM Engineer who thrives on client engagement, rapid experimentation, and taking ownership of delivery in fast-paced environments.

Apply now to help shape the next generation of enterprise AI and agentic system deployments.