Key Facts

Remote From:

Greece

Category: Product Analyst

Full time

Senior (5-10 years)

English

Hard Skills

Prompt Engineering Cloud Applications Data Modeling Web Applications Exception Handling

Other Skills

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Mobile Apps
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Collaboration
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Problem Solving

Roles & Responsibilities

Experience in AI feature delivery and integration
Proficiency in LLMs and ML models
Strong background in prompt engineering
Ability to collaborate with cross-functional teams

Requirements:

Lead the end-to-end development of AI-powered features
Integrate LLMs and ML models into existing applications
Craft, test, and optimize system prompts
Collaborate with designers and product managers in agile ceremonies

Job description

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio.

We are currently looking for a dedicated Senior AI Product Engineer to join one of our clients' teams. If you're looking for an exciting opportunity to grow in an innovative environment, this could be the perfect fit for you.

Key Responsibilities

AI Feature Delivery & Integration

Lead the end-to-end development of AI-powered features, from initial prototyping to production deployment.
Integrate LLMs and ML models into existing web/mobile applications and backend APIs.
Design "AI-first" user journeys, ensuring features like autocomplete, summarization, and intelligent search are intuitive and high-performing.

2. Prompt Engineering & Agentic Workflows

Craft, test, and optimize complex system prompts to ensure high-quality, safe, and consistent model outputs.
Build and manage Agentic workflows (using frameworks like LangChain, CrewAI, or AutoGen) to automate multi-step business processes.
Implement robust error-handling and "fallback" logic for non-deterministic AI responses to maintain a seamless UX.

3. Squad Collaboration (The "Product" Side)

Act as the AI subject matter expert within your Product Squad, helping PMs understand what is technically feasible vs. what is "AI hype."
Work closely with UX/UI designers to handle AI-specific design challenges (e.g., streaming text latency, "thinking" states, and user feedback loops).
Participate in agile ceremonies, contributing to grooming, planning, and rapid iteration.

4. Hybrid Implementation

Develop applications that can switch seamlessly between Cloud APIs (for speed/innovation) and On-Premises models (for data privacy and cost-efficiency).
Ensure that AI features are performant across different hosting environments without sacrificing user experience.