AI Agentic Engineer

Software Engineer

Required Skills & Experience:

• 6+ years of professional software engineering experience, with a strong track record of designing and delivering complex, scalable distributed systems.

• AI 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.

• Platform 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.

• Proficiency in Python and/or Java; strong grasp of multiple tech stacks and cloud-native development on AWS and/or GCP.

• Experience with RESTful services, event-driven architectures, and backend databases (SQL, NoSQL, or cloud-native datastores).

• Familiarity with containerization technologies (Kubernetes, Docker) and modern CI/CD practices and tools (e.g., GitLab).

• Demonstrated 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.

• Strong emphasis on building observability into systems — real-time alerting, dashboards, metrics, and performance accountability.

• Experience working with cross-functional teams including product, business, infrastructure, and security stakeholders.

• Strong verbal and written communication skills; ability to articulate complex technical decisions to both technical and non-technical audiences.

• Agile development experience (Scrum, Kanban, Lean, or similar) with a continuous improvement and quality mindset.

• Bachelor'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.