Forward Deployed Engineer
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Job Description
Forward Deployed Engineer building applied AI, LLM systems, and ML infrastructure. Partner-facing delivery from discovery through deployment and reliability for enterprise and research teams.
Responsibilities:
- Work directly with AI research teams and enterprise partners to define research goals, technical requirements, and project direction
- Translate ambiguous AI and machine-learning problems into clearly scoped technical projects
- Act as a technical implementation partner across research, engineering, product, and stakeholder teams
- Move between research questions, technical architecture, hands-on engineering, and partner-facing execution
- Own technical systems from discovery through implementation, deployment, iteration, and ongoing reliability
- Build large-scale data-intelligence systems for collecting, organising, evaluating, and improving training and evaluation data
- Implement ML pipelines for data curation, model training, evaluation, experimentation, and continuous improvement
- Develop infrastructure for model inference, experimentation, evaluation, and deployment
- Design reliable workflows supporting advanced AI research and production environments
- Build and maintain systems supporting complex data and machine-learning workloads
- Develop LLM applications including multi-agent systems, tool-using agents, RAG workflows, and human-in-the-loop systems
- Build evaluation harnesses and infrastructure for assessing agent and model behaviour
- Develop systems that extend AI experiments into reliable, repeatable, multi-turn workflows
- Implement agentic automation across technically complex business and research processes
- Apply modern LLM tooling and model-evaluation approaches to production-oriented AI systems
- Design data taxonomies, labelling systems, evaluation rubrics, and quality frameworks
- Improve dataset structure and quality to support stronger model performance and research outcomes
- Develop workflows for dataset curation, annotation, validation, and quality assurance
- Analyse data and model behaviour to identify opportunities for system improvement
- Apply rigorous standards to training data, evaluation datasets, and research workflows
- Project scope and research priorities may evolve depending on technical and partner requirements
Requirements:
- Strong professional experience as a software, machine-learning, applied AI, or infrastructure engineer
- Advanced Python engineering skills with experience building and shipping production systems end to end
- Practical experience with LLMs, agentic systems, multi-turn workflows, tool use, RAG, or AI automation
- Experience building or maintaining data pipelines, ML infrastructure, model-evaluation systems, or research workflows
- Strong understanding of data quality, taxonomy design, labelling workflows, and dataset curation for AI systems
- Ability to operate independently in ambiguous, partner-facing environments with strong technical and product ownership
- Comfortable working directly with researchers, technical partners, founders, and enterprise stakeholders
- Experience within a startup, AI infrastructure company, applied AI organisation, or research-focused engineering team is advantageous
- Experience building multi-turn agents, agent-evaluation systems, workflow automation, or human-in-the-loop AI is advantageous
- Familiarity with modern LLM tooling, agent frameworks, model-evaluation stacks, and ML experimentation platforms is beneficial
- Experience acting as a technical partner to external customers, research teams, or strategic enterprise accounts is strongly valued
- Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
Benefits:
- Remote work
- Travel required as needed for partner-facing collaboration and project execution
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