Applied AI Engineer (Squad Lead)

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

About the Product:

This is a hyper-scale fintech platform trusted by over 40 million users worldwide, offering access to multi-asset trading - including stocks, indices, currencies, and digital assets.

The product operates as a regulated, data-intensive financial ecosystem, processing massive volumes of transactions and real-time customer interactions daily. It’s well-funded, profitable, and known for pushing innovation in AI, automation, and data governance across its operations. Advertising presence is strong (especially in the UK), but behind the scenes is a serious engineering culture solving hard, real-world problems at scale.

About the Role:

We are looking for an Applied AI Engineer (Squad Lead) to join a fast-paced, AI-first environment where AI is already a core part of how the company operates. This is not a role where you’ll be adding an AI feature to an existing product or experimenting with isolated prototypes. You’ll take real business challenges, work directly with stakeholders to understand what needs to change, and turn those problems into production-grade AI agents and applications — end to end, from idea and POC to deployment and continuous improvement.

This is a deeply hands-on role with significant autonomy and ownership. You’ll work with modern GenAI and agentic technologies, use AI-native development workflows daily, and see the impact of your solutions quickly. As the squad grows, you’ll also guide other engineers while remaining hands-on.

Key Responsibilities:

• Design, build, and deploy AI-powered agents and applications from concept through production.
• Develop and maintain AI pipelines for data ingestion, embeddings, retrieval, and LLM orchestration.
• Write high-quality backend and frontend code to embed AI into user-facing tools and internal platforms.
• Build APIs and integrations with internal systems (CRM, databases, third-party services) for seamless automation and data flow.
• Work with structured, unstructured, and vectorized data to support intelligent workflows and decision-making.
• Continuously monitor, evaluate, and iterate to improve system performance, reliability, and cost efficiency.
• Design and implement scalable, secure, observable, cloud-based solutions.
• Own production systems, including debugging, enhancements, and ongoing optimization.
• Collaborate closely with product and business stakeholders to translate requirements into high-impact solutions.

Required Competence and Skills:

• 5+ years of software engineering experience (backend or full-stack).
• Proven daily use of AI-native development workflows (e.g., Cursor, Claude Code) for production systems.
• Hands-on experience building production AI agents, including RAG, multi-agent orchestration, tool-calling, structured outputs/validation, memory, and workflow management.
• Experience with agent orchestration and evaluation frameworks (LangChain, LangGraph, LangSmith, Semantic Kernel or equivalent).
• Track record of deploying LLM-powered systems in production.
• Strong backend and modern frontend development skills for complex workflows.
• Experience with cloud services (Azure, AWS, or GCP) and designing scalable architectures.
• Familiarity with vector databases and embedding pipelines.
• Deep understanding of system design, scalability, observability, and security best practices.
• Experience leading small development teams or owning a technical domain.
• Excellent communication, stakeholder management, and problem-solving skills.
• Fluent written and verbal English.

Nice to Have:

• Experience building AI systems in regulated or compliance-driven environments.
• Background working with internal teams and platforms (e.g., customer service, retention, operations, compliance).
• Strong understanding of business process automation and operational intelligence.
• Familiarity with event-driven architectures and distributed systems.
• Experience implementing AI evaluation frameworks and automated testing for LLM systems.