Senior AI Engineer m/f/d

Senior AI Engineer m/f/d Overview

This is a hands-on senior AI engineering role focused on turning AI capabilities into real product features and dependable production workflows. The position is based in the United Kingdom and is offered through Jobgether on behalf of a partner company, which will handle applications and the next stages of the hiring process. The environment is fast-paced and product-led, with a strong emphasis on ownership, experimentation, and shipping practical solutions that create measurable value.

The role is designed for an engineer who enjoys taking AI ideas from prototype to production and improving them over time. You will work on LLM-powered functionality inside large-scale technology products, combining software engineering with applied AI techniques such as retrieval, embeddings, agents, orchestration, and tool use. A major part of the job is not only building features, but also making them reliable, observable, and maintainable in live systems.

What you will do

• Design, implement, deploy, and support production LLM features that are embedded in customer-facing products and internal operational workflows.
• Build the backend services, APIs, integrations, and infrastructure needed to connect AI capabilities with product functionality.
• Contribute to full-stack and user-facing development when required so the AI experience works end to end.
• Apply production-ready approaches involving LLMs, retrieval-augmented generation, embeddings, vector search, agent-based systems, orchestration, and tool calling.
• Create reusable engineering components, abstractions, and patterns that improve delivery speed, consistency, and maintainability across the team.
• Develop and improve evaluation frameworks, observability tooling, monitoring, and feedback mechanisms for AI features in production.
• Measure and optimize system quality, latency, reliability, scalability, and inference cost.
• Investigate production incidents, analyze telemetry, and use operational data and user feedback to improve AI behavior over time.
• Work closely with Product, ML, and Engineering stakeholders to convert business problems into practical AI solutions.
• Prototype new ideas quickly, validate them, and then turn the successful ones into robust production systems.
• Participate in architecture and technical decision-making, including trade-offs around scale, sustainability, and long-term maintainability.

What the company is looking for

• Proven practical experience building and shipping LLM-based features or applications into production.
• Hands-on familiarity with RAG, embeddings, vector search, agentic workflows, LLM APIs, orchestration, and tool-calling patterns.
• Strong understanding of production LLM concerns such as evaluation, observability, failure modes, latency, reliability, and cost management.
• Experience using telemetry, evaluation results, production signals, or user feedback to improve AI features.
• A solid software engineering foundation, including experience with APIs, backend services, and distributed systems in production.
• Full-stack experience with tools such as Angular, JavaScript/TypeScript, Python, Node.js, or similar technologies is considered a strong advantage.
• The ability to produce clean, maintainable, well-structured code and take responsibility for solutions from implementation through production.
• Comfort working across backend systems, AI infrastructure, and user-facing product functionality.
• Experience with AWS or similar cloud environments, CI/CD pipelines, and live production deployments.
• A strong product mindset with a bias toward shipping, rapid iteration, pragmatic problem-solving, and outcomes that can be measured.
• Comfort operating in ambiguous, fast-changing environments where requirements continue to evolve.
• Excellent communication and collaboration skills, especially when working with Product, ML, and Engineering teams.
• The ability to challenge requirements constructively, explain technical trade-offs clearly, and translate AI complexity into practical product decisions.
• A mindset focused on maintainability and reuse, with an emphasis on building real production systems rather than one-off demonstrations.
• Additional experience with multi-agent systems, MCP, guardrails, moderation, safety checks, prompt and context management, routing, caching, or fallback strategies would be beneficial.
• Experience with Docker and cloud platforms such as AWS, GCP, or Azure is also helpful.
• Experience with modern frontend development and customer-facing web applications is an advantage.
• This role is centered on applied AI engineering rather than heavy model training, academic research, custom ML algorithm development, or theoretical optimization work.

What is offered

• A highly visible role with direct access to senior leadership and real influence over product and engineering decisions.
• Strong opportunities for career development and continuous learning in applied AI and software engineering.
• The chance to work with experienced technology, product, and industry professionals on high-impact AI initiatives.
• An international, multicultural working environment with colleagues located across multiple countries.
• A flexible work-from-home arrangement.
• A $500 home-office setup allowance to support a productive remote workspace.
• A $1,000 annual learning and development allowance to support professional growth and skill building.
• Visa and relocation support when required.
• The opportunity to work on production-scale AI applications using modern LLM technologies.
• High ownership and autonomy, with the chance to take AI solutions from experimentation through deployment and ongoing improvement.
• Competitive compensation aligned with experience, skills, and market standards.

Application and hiring process

Applications are submitted through Jobgether, which uses an AI-assisted matching process to review candidates quickly, consistently, and fairly against the role’s core requirements. The system identifies the strongest matches and shares that shortlist directly with the hiring company. From there, the partner company manages the final decision-making and any next steps such as interviews or assessments.

By applying, candidates acknowledge that Jobgether will process personal data for recruitment purposes and share relevant information with the hiring employer. This processing is described as being based on legitimate interest and pre-contractual measures under applicable data protection laws, including GDPR. Candidates are also informed that they may exercise data rights such as access, correction, deletion, and objection. The page also notes that AI tools may assist parts of the recruitment process, such as reviewing applications or identifying inconsistencies, but final hiring decisions are made by humans.

Degree Requirement: Not Specified

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