Senior Applied AI Engineer

About This Role

We are partnering with a global online trading organization that is transitioning into a fully AI-first engineering company. AI is already embedded in production across customer operations, internal workflows, compliance processes, and engineering systems. The next phase is to deepen and scale this transformation by building robust, reliable, AI-native architectures that support real-time, high-stakes environments.

We are seeking strong Applied AI Engineers with a solid engineering foundation who have moved beyond experimentation and are actively building and shipping AI systems in production. Seniority will be determined based on depth of experience, architectural ownership, and impact.

Key Responsibilities

• Design, build, and scale AI-powered systems that operate reliably in production environments
• Architect LLM-based workflows, RAG pipelines, and multi-agent systems
• Translate business and operational problems into scalable AI-driven solutions
• Ensure reliability, observability, and performance of AI systems under real user load
• Balance deterministic systems, traditional backend engineering, and probabilistic AI models
• Collaborate across engineering, operations, and compliance teams to deliver end-to-end solutions
• Contribute to long-term AI architecture and technical direction

Candidate Profile

• 7 to 15+ years of overall engineering experience
• Strong background in backend or full-stack development
• Hands-on experience building and deploying AI-powered systems in production
• Experience in product-led, fast-paced environments such as fintech, trading platforms, crypto, or high-scale tech companies
• Ability to operate in ambiguity and translate broad objectives into technical roadmaps

Technical Expertise

• Strong proficiency in at least one core language such as Python, TypeScript, Go, C++, or Rust
• Experience integrating LLMs using APIs such as OpenAI or Anthropic
• Experience designing and implementing RAG pipelines and working with vector databases such as Pinecone, Milvus, or similar
• Understanding of prompt engineering, model evaluation, and strategies to manage hallucination and reliability
• Experience with cloud infrastructure (AWS or equivalent), Docker, Kubernetes, and distributed systems
• Familiarity with databases such as PostgreSQL and Redis
• Exposure to MLOps, fine-tuning, monitoring, and performance optimisation is an advantage
• What We Are Not Looking For
• Profiles limited to research, notebooks, or proof-of-concept AI projects without production exposure
• Pure people managers who are no longer hands-on
• Consulting or IT services profiles without direct product ownership
• Engineers who have only experimented with AI tools without building scalable systems

This opportunity is suited to engineers who think in systems, understand trade-offs between speed and robustness, and have built AI solutions that handle real users and real operational constraints. Candidates should be prepared to discuss specific systems they have designed, scale handled, architectural decisions made, and lessons learned from failures in production environments.