Applied AI Engineer – (Remote) (Europe or UK)

Sivadconsulting; Greater London; À distance; GBP 84 000 - 125 000; Plein temps

Résumé du poste

Sivadconsulting is hiring an Applied AI Engineer – Remote (Europe or UK). You will design and build core primitives for agentic systems, focusing on memory, context management, tool-calling, and orchestration.

You will push for reusable infrastructure with reliable production-grade performance and develop evaluation frameworks to measure agent effectiveness. You will advance LLM-driven software, create workflows for coding agents and subagents, and ensure robust testing, debugging, and

Qualifications

• Experience delivering complex systems from idea through production.
• Hands-on experience building AI or agentic systems that operated in production.
• Experience addressing real-world AI challenges: reliability, latency, context management, failure modes, evaluation, observability.
• Strong understanding of agents, tool use, structured outputs, context management, orchestration, evaluation, and feedback loops.
• Advanced practical use of modern LLMs and building reliable workflows around probabilistic systems.
• Experience using coding agents and agentic development workflows as part of engineering process.
• Experience decomposing complex tasks across agents and designing verification mechanisms.
• Strong architectural judgment on when to use LLMs, deterministic software, or human input.
• Fluent English with strong communication skills.

Responsabilités

• Design and build the core primitives behind our agentic systems (memory, context, tool-calling, orchestration, feedback loops).
• Move from one-off AI solutions toward reusable infrastructure with predictable reliability.
• Build the evaluation framework to understand agent performance and reasons for success or failure.
• Make testing, tracing, debugging, and evaluating AI systems foundational to engineering practices.
• Develop systems to assess agentic workflows before production deployment.
• Advance how we use LLMs to build software themselves.
• Create workflows where coding agents and subagents explore repositories, implement changes, review architecture, check conventions, run evaluations, and iterate with minimal human coordination.
• Use agentic workflows extensively in day-to-day development.
• Design systems where specialized agents, tools, deterministic software, and humans collaborate effectively.
• Decide when problems are best solved with LLMs, traditional software, or multiple coordinated agents.
• Bridge between complex operational workflows and engineering.
• Collaborate with operations and product teams to identify automation opportunities and turn them into production systems.
• Help define architectural patterns for agentic systems as the company scales.
• Be an early core member with influence over technical approach, tooling, and engineering standards.

Connaissances

Software engineering

AI systems

Agentic systems

LLM workflows

Context management

Orchestration

Evaluation feedback

Reliability

Latency

Observability

Tool use

TypeScript/Node.js

Python

English communication

Startup mentality

Description du poste

Applied AI Engineer – (Remote) (Europe or UK - Required)

100k – 150k Euro

Responsibilities

• Design and build the core primitives behind our agentic systems, including memory, context management, tool-calling, orchestration, and feedback loops
• Move us from one-off AI solutions toward reusable infrastructure where new workflows can be introduced quickly and with predictable reliability
• Build the evaluation framework that allows us to understand how our agents perform and why they succeed or fail
• Make testing, tracing, debugging, and evaluating AI systems as fundamental to our engineering process as unit testing traditional software
• Develop the systems that allow us to confidently assess an agentic workflow before deploying it into production
• Push forward how we use LLMs to build software itself
• Create workflows where coding agents and specialized subagents can explore repositories, implement changes, review architecture, check conventions, run evaluations, and iterate with minimal human coordination
• Use agentic engineering workflows extensively in your own day-to-day development
• Design systems where specialized agents, tools, deterministic software, and humans work together effectively
• Understand when a problem should be solved with an LLM, when traditional software is the better solution, and when multiple coordinated agents can materially improve the outcome
• Act as the bridge between complex operational workflows and engineering
• Work closely with our operational and product teams to understand how work actually happens across acquired agencies, identify the highest-leverage opportunities for automation, and turn them into reliable production systems
• Help define the architectural patterns we use for agentic systems as the company scales
• You won't be joining as engineer #20 on an established AI platform. You will be an early core member of the team with significant influence over our technical approach, tooling, and engineering standards

Requirements

• Strong software engineering background with experience independently delivering complex systems from idea through to production
• Hands-on experience building **AI or agentic systems that have operated in production**
• Experience dealing with real-world AI challenges such as reliability, latency, context management, failure modes, evaluation, and observability
• Strong understanding of **agents, tool use, structured outputs, context management, orchestration, evaluation, and feedback loops**
• Advanced practical use of modern LLMs — not simply using ChatGPT or Claude occasionally, but understanding how to build reliable workflows around probabilistic systems
• Experience using **coding agents and agentic development workflows** as part of your own engineering process
• Experience decomposing complex engineering tasks across agents or specialized subagents and designing mechanisms for them to verify and improve their outputs
• Strong architectural judgment and ability to decide when to use LLMs, deterministic software, human intervention, or a combination of approaches
• Experience with **TypeScript / Node.js**, or strong experience building AI systems in Python with the ability and willingness to apply that knowledge in a TypeScript-first environment
• High autonomy, ownership, and comfort making decisions under ambiguity
• Strong product judgment and ability to connect technical decisions to operational and business outcomes
• Strong communication skills and fluency in English
• Startup mentality: resilience, adaptability, and ability to thrive in a fast-paced environment