Applied AI Engineer

About the Company:

Personetics is shaping the Cognitive Banking era, harnessing AI to help banks anticipate customer needs, provide actionable insights, and deliver intelligent financial guidance. Our platform continuously analyzes and leverages real-time transactional data, enabling banks to proactively support customers in managing their finances and reaching their goals. As industry leaders—yes, we really are leaders—we partner with the world’s top financial institutions, empowering over 150 million customers monthly across 35 global markets from offices in New York, London, Singapore, São Paulo, and Tel Aviv.

About the Position:

We are looking for a highly skilled Applied AI Engineer with a strong engineering mindset to bridge the gap between research and production. In this role, you will be responsible for validating AI models developed by our Data Science team against real-world production systems, and then leading their optimization, deployment, and ongoing maintenance. You will be part of the R&D team, working closely with engineers, data scientists, and product managers to ensure our AI solutions are scalable, reliable, and deliver long-term value. If you enjoy working at the intersection of AI and engineering — bringing models to life in production, optimizing for performance, and building reliable systems — this role is for you!

Responsibilities:

AI System Design & Productionization:

• Lead the transition of AI models from proof-of-concept to full-scale production, ensuring they

meet architectural, scalability, and performance standards.

• Ensure models developed in research align with our product requirements, system architecture,

and real-world constraints.

• Collaborate with DevOps and cloud infrastructure teams to deploy AI solutions in robust,

scalable environments.

AI Model Validation, Optimization & Maintenance:

• Partner with the Data Science team to review AI models, validate data assumptions, and assess

methodologies.

• Enhance and troubleshoot models by integrating new features and improving

efficiency.

• Own model versioning and implement drift detection processes to ensure long-term model

reliability and integrity.

Requirements:

• 3-5 years of experience in ML Engineering, AI models deployment or MLOps roles.
• Hands-on experience optimizing AI models for real-time or large-scale applications.
• Strong proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
• Production-level coding skills in Python, Java, or other languages used in AI/ML deployment.
• Experience deploying AI solutions in cloud environments (AWS, GCP, or Azure) and
• working with containerized infrastructure (Kubernetes, Docker).
• Excellent analytical and problem-solving abilities, with a focus on model optimization, performance tuning, and long-term reliability.

Nice to Have:

• Experience working with vector databases and generative AI APIs (e.g., OpenAI,
• Anthropic).
• Familiarity with MLOps practices, including automated model training pipelines, CI/CD for
• AI, and real-time inference systems.
• Understanding of AI governance, compliance, security, and responsible AI principles.