About Gentek
Gentek is an agentic AI company rebuilding the regulatory and operational reporting stack for global financial and energy markets. We replace the patchwork of manual checks, brittle scripts, and disparate frameworks that banks and energy firms rely on today with AI agents that reason about rules, reconcile data, and resolve exceptions end-to-end. We're trusted by leading organisations and we partner with infrastructure providers to bring agentic workflows financial institutions, and leading European energy brokers.
About the Role
As a Forward AI Engineer, at Gentek, you own client engagements end-to-end. You run the calls, scope the work, write the plan, and drive a technical team to deliver it. You are the day-to-day face of Gentek to clients. You don't have to write the code yourself but you understand AI well enough to shape solutions, challenge estimates, and translate client requirements.
Core Responsibilities
- Project Management: discovery, requirements, project plan, milestones, risks, and stakeholder alignment. You set the cadence and you are accountable to the client.
- Client Engagement: Lead calls, workshops, and written communications in both languages. Turn ambiguous business needs into structured requirements and a deliverable plan.
- Solution Shaping: Work with engineers to scope the full stack ie data, models, agent workflows, integrations, and user-facing outputs and make sure what gets built solves the client's problem.
- Technical Fluency: Stay current on multi-agent systems, RAG, and modern AI frameworks enough to scope realistic engagements and hold credible technical conversations with clients and engineers.
- PoC to Production: Drive the path from requirements through PoC to production, presenting progress and outcomes to internal and external stakeholders at each stage.
Required Skills & Experience
- Project Management: Track record running client-facing technical projects end-to-end ie scoping, planning, milestones, risks, and stakeholder communication. You own the plan with minimal oversight.
- Fluent French (near-native): You can lead meetings, run workshops, and write deliverables in French.
- AI Literacy: Working understanding of modern AI frameworks (LangChain, LangGraph, LlamaIndex), RAG, multi-agent architectures, and evaluation — enough to scope work, challenge estimates, and speak credibly with engineers and clients. You don't need to ship the code.
Nice to have
- Hands-on AI engineering experience: Python, prompt engineering, RAG, agent frameworks, evaluation. You won't be expected to build, but the more you can, the more credibility you carry into client conversations.
- Hands-on Agentic Harness experience like Claude : API, Claude Code, or agentic workflows.Prior experience is a real differentiator.
- Experience in financial or regulatory reporting domains (e.g. trade reporting, fund reporting, or market data workflows).
- Exposure to smaller, domain-specific models (SLMs), including fine-tuning and deployment.
- Familiarity with cloud platforms (AWS, Azure, or GCP) for deploying AI services in production.
Soft Skills
- Stakeholder Management: Confident leading client conversations, managing expectations, surfacing risks early, and keeping multiple parties aligned across the lifecycle of a project.
- Ownership: Willing to take full responsibility for outcomes in a fast-paced, delivery-focused environment.
- Technical Diplomacy: Translate business needs into clear technical specifications and explain complex AI concepts to non-technical stakeholders.
- Pragmatic Delivery: Balance speed and engineering quality, making sound trade-offs to deliver effective solutions under real constraints.