AI Agentic Engineer

Required Skills & Experience
• \t6+ years of professional software engineering experience, with a strong track record of designing and delivering complex, scalable distributed systems.\n•\tAI Fluency — Required: Hands-on experience working with LLMs, foundation model APIs (OpenAI, Anthropic, Google, etc.), prompt engineering, retrieval-augmented generation (RAG) architectures, and embedding-based search in production environments.\n•\tPlatform engineering in AI teams — You have contributed to or built shared AI infrastructure or developer platforms within an AI-focused product organization, enabling other teams to build reliable AI-powered features on top of your foundation.\n•\tProficiency in Python and/or Java; strong grasp of multiple tech stacks and cloud-native development on AWS and/or GCP.\n•\tExperience with RESTful services, event-driven architectures, and backend databases (SQL, NoSQL, or cloud-native datastores).\n•\tFamiliarity with containerization technologies (Kubernetes, Docker) and modern CI/CD practices and tools (e.g., GitLab).\n•\tDemonstrated ability to design systems spanning multiple weeks or months of work, incorporating a full team’s worth of engineers, and balancing short and long-term trade-offs.\n•\tStrong emphasis on building observability into systems — real-time alerting, dashboards, metrics, and performance accountability.\n•\tExperience working with cross-functional teams including product, business, infrastructure, and security stakeholders.\n•\tStrong verbal and written communication skills; ability to articulate complex technical decisions to both technical and non-technical audiences.\n•\tAgile development experience (Scrum, Kanban, Lean, or similar) with a continuous improvement and quality mindset.\n•\tBachelor’s or master’s degree in computer science, Engineering, or equivalent practical experience.

Job Description

On a day-to-day basis, this person designs and builds the core AI platform capabilities that enable teams across the company to develop and deploy AI-powered solutions. They collaborate closely with engineering, business, infrastructure, and security teams to architect scalable systems, integrate emerging AI technologies, review code and designs, improve platform reliability and performance, mentor engineers, and ensure all work meets engineering and security standards while balancing immediate business needs with long-term platform growth.