AI Engineer

About FasterPay

FasterPay is a fast-growing fintech company that helps businesses expand globally through secure, seamless payment solutions. We’re transparent, flexible, and focused on building trust and innovation across the global payments ecosystem.

Our mission is simple: make global commerce smarter, safer, and more efficient — and we’re looking for ambitious people who want to grow with us.

Who We’re Looking For

We're looking for AI Engineers at all levels—whether you're just starting out, building your expertise, or ready to lead technical initiatives. What matters most is your passion for AI and your drive to build systems that make real impact.

We value:

• Curiosity & Eagerness to Learn – You love exploring AI and want to know what it can do
• Builder Mindset – You want to create real products that solve real problems
• Fast Learner – You're not afraid of challenges and learn new things every day
• Good Teamwork – You enjoy solving problems with your team
• Honesty & Transparency – You communicate truthfully and ask when you're unsure
• Global Mindset – You're comfortable working with international teams

What You will Do:

Join our global team to build AI systems for our fintech platform. Your responsibilities will scale with your experience level:

Core Responsibilities (All Levels)

• Develop and deploy machine learning models that solve real business problems
• Build AI applications: computer vision, LLMs, audio processing, video processing, NLP, and more
• Create intelligent data pipelines that automatically process and learn from data
• Experiment with the latest AI technologies and techniques
• Work with real code, deploy to production, serve real users
• Collaborate with different teams across the company and globally

Additional Responsibilities By Levels

Mid Level:

• Own complete features or components of AI systems
• Comprehend AI technologies, analyze workflows, and train other teams to use AI
• Make technical decisions on implementation approaches
• Mentor junior engineers and review their code
• Contribute to system architecture and design discussions

Senior Level:

• Lead the design and architecture of major AI systems
• Drive technical strategy and roadmap for AI initiatives
• Evaluate and introduce new AI technologies to the team
• Mentor and guide junior and mid-level engineers
• Collaborate with product and business teams on AI strategy
• Make high-impact technical decisions across multiple projects
  

What You Will learn:

• Hands-on experience building AI systems end-to-end
• Work across different AI domains: computer vision, LLMs, audio/video processing, NLP
• Deploy models to production and monitor their performance
• Best practices in MLOps, cloud platforms, and software engineering
• How to build AI that protects sensitive data while enabling smart decision-making

Must Have:

• Bachelor's degree in Computer Science, Software Engineering, Data Science, AI, or related field
• 2-4 years of experience in AI/ML engineering
• Strong Python programming skills with production experience
• Proven ability to comprehend AI technologies, analyze workflows, and train teams to use AI
• Advanced prompt engineering skills and experience with AI coding assistants
• Solid understanding of ML algorithms and when to apply them
• Experience deploying and maintaining ML models in production
• Proficiency with multiple ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Experience with at least 2 AI domains (NLP, computer vision, audio/video processing, LLMs)
• Strong communication skills to explain technical concepts to non-technical stakeholders
• English proficiency for global collaboration

Nice to Have:

• Node.js, PHP, Golang skills
• Experience with MLOps tools and practices
• Cloud platform expertise (AWS, GCP, Azure)
• Experience with large-scale data processing
• Contributions to open-source AI projects
• Published papers or blog posts on AI topics
• Additional languages

Nice-To-Have (All Levels)

• Knowledge of cloud platforms (AWS, GCP, Azure) or Docker
• Understanding of MLOps and model deployment practices
• Interest in AI safety, privacy-preserving ML, or responsible AI
• Willingness to travel for business