AI Native Software Engineer

About the role

We are a forward‑thinking services company that helps enterprise clients adopt AI‑native, agent‑powered workflows at scale. As an AI Native Software Engineer you will design, build and operate AI agents that augment business processes and run reliably in production environments.

Key responsibilities

• Partner directly with client stakeholders to define use cases, prototype, and deploy robust, secure agentic workflows.
• Design and implement enterprise‑ready AI agents with retrieval, orchestration, policy‑based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Develop abstraction layers across AI providers (Anthropic, Google, OpenAI) to enable multi‑provider integration.
• Leverage containerization (Kubernetes, Docker), microservices, serverless, event‑driven architectures, CI/CD, and observability stacks to deliver scalable AI‑native systems.
• Own deployment, monitoring, and troubleshooting of services in production.
• Tailor agentic applications for verticals such as finance, healthcare, and retail, translating domain‑specific workflows into AI solutions.
• Lead design workshops, proofs of concept, and code‑with sessions to foster client adoption.
• Define and use metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.

Required profile

• Strong foundation in building cloud‑native solutions and hands‑on experience with enterprise‑scale agentic systems.
• Critical thinker who thrives in ambiguous environments and delivers concrete results.
• Ability to act as both technologist and trusted advisor to senior client stakeholders.
• Experience leading complex technical projects or owning end‑to‑end technical areas as a senior individual contributor.

Required skills

• Kubernetes
• Docker
• CI/CD pipelines
• Serverless architectures
• Microservices
• Event‑driven architectures
• Observability and monitoring tools
• AI provider integration (Anthropic, Google, OpenAI)
• SDK and shared library development
• Retrieval, orchestration, policy‑based routing, tool invocation
• Evaluation harnesses and lifecycle observability