Careers
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
- Lead the transition of AI models from proof-of-concept to full-scale production, ensuring they meet architectural, scalability, and performance standards.
- Build observability and troubleshooting tools for AI services in production, including logging, performance tracking, and failure analysis pipelines.
- Optimize inference performance, including latency, resource usage, and throughput, while maintaining model quality.
- Manage model versioning and deployment readiness, including handoff processes, rollback plans, and configuration management.
- Partner with the Data Science team to assess model readiness for production, validate input and output compatibility, and ensure assumptions align with real-world system behavior.
- Collaborate cross-functionally with DevOps, backend engineers and data scientists to ensure scalable, secure, and cost-effective deployment of ML models
Requirements
- 3-5 years of experience in ML Engineering, AI models deployment or MLOps roles.
- Strong software engineering background with hands-on experience (Python or Java preferred) building and maintaining production ML services
- Solid understanding of machine learning systems and inference pipelines
- Familiarity with monitoring practices and production diagnostics for ML services (e.g., metrics, logs, alerting)
- Proven experience in optimizing AI models for performance (response-time, memory, CPU usage) particularly in real-time or large-scale environments.
- Strong proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
- Experience deploying AI solutions in cloud environments (AWS, GCP, or Azure)
Nice to have
- Familiarity with GenAI production environments — working with LLMs, vector databases, and third-party generative AI APIs.
- Understanding of AI governance, compliance, security, and responsible AI principles.
