Senior AI Engineer

Role overview Define how intelligence gets built into every product a client engagement ships. This senior role goes well beyond proof-of-concept work and focuses on production-grade AI systems where latency, hallucination, cost, and context drift are real constraints. You will lead AI architecture decisions, set technical direction across client products, and mentor full-stack engineers on integration best practices.

Responsibilities - Lead AI architecture and implementation across client product engagements, from model selection through production deployment. - Design and build production LLM pipelines, including retrieval-augmented generation architectures, agent systems, and domain-specific fine-tuning workflows. - Evaluate AI approaches model-agnostically, choosing the right tool based on product needs rather than hype. - Contribute to the discovery phase by defining AI feasibility, data requirements, and integration strategy before a single sprint begins. - Mentor full-stack engineers on AI integration best practices and architectural patterns. - Track new models, frameworks, and techniques, and bring useful developments back to the team. - Contribute to technical thought leadership through blog posts, internal playbooks, and open-source work.

Requirements - Five or more years of software engineering experience, with at least two years focused on production AI or machine learning systems. - Deep experience with LLM integration, including prompt engineering, retrieval-augmented generation, fine-tuning, and agent architectures at production scale. - Strong Python background and familiarity with LLM orchestration frameworks and vector database ecosystems. - Product-level judgment: a technically correct solution that ships late or confuses the user has failed. - Clear written and verbal communication, with the ability to write a technical spec that a client's CTO can review and approve. - Held opinions about AI architecture that can be defended and updated when better arguments appear.

Nice to have - Computer vision pipelines. - Predictive analytics and time-series modeling. - Client-facing or consultancy experience. - Published technical writing or open-source contributions. - Data science background.