AI Engineer

We’re looking for an experienced AI Engineer to join a fast-moving technology team building intelligent software products powered by machine learning and generative AI. This role sits at the intersection of AI research and production engineering. You’ll be responsible for prototyping new models, LLM and prompting approaches, and turning successful concepts into reliable, scalable and cost-efficient AI solutions. Working closely with product and software engineering teams, you’ll help translate complex and ambiguous business challenges into practical AI features that are deployed into production and deliver real-world value. The ideal candidate is comfortable moving from experimentation and proof-of-concept development through to productionisation, optimisation and ongoing improvement of AI systems. • Design, build, and deploy machine learning and LLM-based systems, including retrieval-augmented generation (RAG) pipelines, fine-tuned models, and agentic workflows. • Own the full lifecycle of AI features: data collection and evaluation, model/prompt selection, experimentation, deployment, and monitoring in production. • Build and maintain data pipelines, embeddings stores, and vector databases that support search, retrieval, and personalization. • Evaluate and integrate third-party model APIs (OpenAI, Anthropic, etc.) alongside open-source and self-hosted models, balancing quality, latency, and cost. • Fine-tune, train, and deploy open-source models on our own infrastructure — including data prep, training/fine-tuning runs, quantization, and self-hosted inference serving at scale. • Establish evaluation frameworks and metrics to measure model quality, drift, hallucination rate, and business impact over time. • Collaborate with full-stack engineers to expose AI capabilities through clean, well-documented APIs and SDKs. • Implement guardrails, safety checks, and monitoring for AI systems running in production. • Stay current with the fast-moving AI/ML landscape and bring back practical recommendations on tools, models, and techniques. • Write clear technical documentation and communicate trade-offs to both technical and non-technical stakeholders. Required Experience • 4+ years of experience building and shipping machine learning or AI-powered systems in production. • Strong Python skills, with hands-on experience using frameworks such as PyTorch, TensorFlow, or similar. • Practical experience with large language models — prompting, fine-tuning, RAG, or agent frameworks (e.g., LangChain, LlamaIndex, or custom implementations). • Hands-on experience training and fine-tuning open-source models (e.g., Llama, Mistral, Qwen) — full fine-tuning or parameter-efficient methods (LoRA/QLoRA) — and deploying them on your own infrastructure rather than relying solely on hosted APIs. • Experience standing up self-hosted inference serving on your own infra (e.g., vLLM, TGI, Triton, Ray Serve), including GPU provisioning, batching, and cost/latency optimization. • Solid understanding of ML fundamentals: model evaluation, overfitting, data leakage, and experimentation methodology. • Experience with vector databases and embedding-based retrieval (e.g., Pinecone, Weaviate, pgvector, FAISS). • Comfort working with cloud infrastructure (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes), including GPU-backed compute.\ • Familiarity with MLOps practices — model versioning, CI/CD for ML, monitoring, and rollback strategies for both hosted and self-hosted models. • Strong software engineering fundamentals: clean code, testing, code review, and API design. • Proficiency working with AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) to accelerate development, while critically reviewing and validating AI-generated code. • Excellent communication skills and comfort working in a remote, async-friendly environment. Nice to Have • Experience with model quantization, distillation, or optimization techniques (e.g., GPTQ, AWQ, ONNX) for efficient self-hosted inference. • Background in NLP, computer vision, or recommendation systems. • Experience with streaming data pipelines (Kafka, Spark) or feature stores. • Contributions to open-source ML/AI tooling. • Prior experience in a startup or fast-growth environment. Please note that only shortlisted candidates will be contacted.