You will design and build AI-driven systems end-to-end, from agent-based workflows and model integrations to backend services, data pipelines, and product-facing capabilities. You will work closely with product, engineering, and research teams across the company to explore new use cases, validate ideas quickly, and bring impactful AI features into production.
This role is ideal for an experienced AI engineer who enjoys moving fast, working across boundaries, and building real production systems, not just experiments. Your work will directly influence how AI is embedded across our platforms and how customers experience secure AI at enterprise scale.
What You Will Need:
8 or more years of professional experience in software engineering, with significant hands-on experience in AI engineering or applied machine learning.
Strong expertise in building AI-powered systems, including LLM-based applications, agents, and orchestration workflows.
Proven experience integrating and operating AI and ML models in production environments.
Proficiency in multiple programming languages, including Python and at least one of the following: .NET, Go, or similar backend languages.
Experience working across diverse technology stacks and product architectures.
Solid understanding of backend system design, APIs, and distributed systems.
Strong experience with databases, including data modeling, performance considerations, and working with both relational and non-relational systems.
Practical experience with DevOps practices, including CI/CD pipelines, containerization, and cloud-based deployment.
Comfort working in cloud environments and modern infrastructure platforms.
Ability to rapidly prototype, iterate, and evolve ideas into production-ready features.
Strong ownership mindset, curiosity, and ability to collaborate across teams.
Nice to Have:
Experience designing and building AI agents for real-world workflows.
Hands-on experience training, fine-tuning, or evaluating machine learning models.
Familiarity with MLOps practices and model lifecycle management.
Experience working in security, cloud platforms, or large-scale SaaS products.
Ability to communicate complex AI concepts clearly to both technical and non-technical audiences.
