Gen AI Engineer

Tkxel; Lahore; On-site; PKR 500,000 - 800,000; Full time

Job summary

Tkxel is seeking a full-stack GenAI engineer who can design, build, and iterate on AI solutions in real time against live business problems. The role sits at the intersection of backend engineering and applied AI, requiring direct client engagement and rapid iteration.

You will design production-grade GenAI systems, build agentic workflows, and translate business problems into deployable AI use cases while communicating trade-offs to stakeholders.

Qualifications

• Strong GenAI expertise with hands-on experience with LLMs, prompt engineering, and multi-agent design.
• Core stack: Python, FastAPI/Django, and Azure or AWS.
• Business understanding and forward-deployed mindset; client-facing and fast iteration.
• Strong communication skills and ability to run discovery sessions.

Responsibilities

• Design and build production-grade GenAI systems for live business problems.
• Build agentic systems that reason, retrieve, and act.
• Work directly with client stakeholders to translate problems into deployable AI use cases.
• Prototype and iterate on-site with the client, compressing feedback loops.
• Own end-to-end solutions—from architecture to handoff.
• Bridge technical decisions and business priorities for non-technical stakeholders.

Skills

GenAI expertise

LLM prompts

Client interaction

Communication skills

Fast iteration

Tools

Python

FastAPI/Django

Azure/AWS

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.