Senior AI Engineer

Role overviewAs a Senior AI Engineer (Agentic AI), you will design, implement, and deploy agentic systems that can plan, reason, and act across tools and data sources. You’ll work across the full lifecycle—from problem discovery and architecture to production deployment and monitoring—ensuring solutions are robust, maintainable, and measurable in impact.What you’ll be responsible for • Own the architecture and delivery of agentic AI systems, including multi-agent workflows, tool orchestration, planning/reasoning components, and decision logic • Partner with product and analytics teams to turn complex business needs into scalable AI services • Build, integrate, and optimize models used inside agentic pipelines with a focus on performance, reliability, and clarity of behavior • Design and implement data/ML pipelines for training, evaluation, and production operation of AI agents • Apply strong software engineering standards (clean APIs, documentation, code reviews, testing) to productionize AI systems • Contribute in an Agile delivery setup (sprints, stand-ups, planning, retrospectives) • Collaborate with platform/DevOps teams on cloud deployment, runtime scalability, observability, and safe releases • Help shape the organization’s AI platform direction (architecture patterns, governance, quality gates) • Define and implement quality practices for agentic systems: automated tests, validation, regressions, monitoring, and performance metrics • Stay current with agentic AI developments and propose pragmatic improvements that can be delivered to production What we’re looking for • Bachelor’s or Master’s degree in Computer Science, AI/ML, or a quantitative field • 5+ years of experience in AI/ML engineering with a proven track record of deploying production solutions • Strong coding skills in Python (or Java/C++), plus solid object-oriented design knowledge • Good understanding of ML fundamentals: modeling, evaluation, statistics, and data analysis • Experience with cloud platforms (AWS / Azure / GCP) and enterprise-grade environments • Working knowledge of DevOps practices: CI/CD, Docker, Kubernetes, infrastructure-as-code • Strong software engineering habits: Git workflows, code reviews, testing methodologies • Data engineering skills (ETL/ELT, warehousing/lakes, big data tooling where relevant) • Agile experience • Familiarity with QA/SQA practices for software + ML systems (incl. monitoring post-deployment) • Strong communication skills and ability to collaborate across engineering/product stakeholders