Job Description

​ We're looking for a full-stack GenAI engineer who can operate directly with client to design, build, and iterate on AI solutions in real time against live business problems, not just ship code from behind a backlog. This role sits at the intersection of backend engineering and applied AI. Key Responsibilities Design and build production-grade GenAI systems: multi-agent architectures, RAG pipelines over large/enterprise-scale document stores, and real-time systems used in daily business operations Build agentic systems that reason, retrieve, and act — not just conversational responders Work directly with client stakeholders to translate ambiguous business problems into scoped, deployable AI use cases Rapidly prototype and iterate on-site/embedded with the client, compressing the feedback loop between idea and working solution Own solutions end-to-end — architecture, implementation, evaluation, and handoff — rather than working purely from fixed specs Bridge technical and business conversations: explain trade-offs to non-technical stakeholders and translate priorities back into technical decisions Requirements Strong GenAI expertise — hands-on experience with LLMs, prompt engineering, RAG, agentic frameworks, and multi-agent design; comfortable working across multiple model providers Core stack: Python, FastAPI/Django, and Azure or AWS Applied depth in the hard parts , e.g.: Permission-aware retrieval at scale Async task orchestration LLM evaluation and observability Business understanding — able to grasp a client's operating model and pressure-test whether a proposed AI use case creates measurable value, not just technical novelty Forward-deployed mindset — thrives working embedded with clients rather than at arm's length; comfortable with ambiguity, direct client exposure, and fast iteration cycles typical of an FDE role Strong communication skills — able to run discovery sessions, present to leadership, and document decisions clearly. ​