Senior Applied AI Engineer

Overview

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.