Gen AI Engineer
Summary
Full-stack GenAI engineer in Lahore who works directly embedded with clients to design, build, and iterate on production AI solutions — multi-agent architectures, RAG pipelines over enterprise document stores — owning solutions end-to-end. Core stack is Python, FastAPI/Django, and Azure or AWS.
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.