AI Native Software Engineer

About the job

Responsibilities

• Design and build production systems encompassing front end, APIs, backend services, integration, data, infrastructure, and AI components.
• Diagnose and resolve production issues across application code, networks, infrastructure, identity, data, or AI workloads.
• Design and deploy cloud-native workloads using containers, Kubernetes, serverless, managed cloud services, and infrastructure as code.
• Utilize native capabilities of AWS, Azure, or GCP for networking, compute, storage, identity, secrets, service communication, and workload isolation.
• Build automated CI/CD pipelines for code deployment, including automated testing, security scanning, artifact management, and release controls.
• Implement secure-by-design practices and instrument systems for observability (logs, metrics, traces, SLOs).
• Lead incident response and root-cause analysis for production issues.
• Design and implement data flows for transactional, streaming, analytical, and unstructured data.
• Design the data and context architecture for AI systems, including retrieval, embeddings, metadata, and knowledge sources.
• Design and implement AI capabilities using frontier and enterprise models, integrating them into enterprise systems and production workflows.
• Build agentic systems with tools, memory, context, planning, human intervention, and policy-controlled execution.
• Develop evaluation frameworks, AI observability, and production safeguards for AI components.
• Apply AI-native development techniques throughout the software lifecycle, including code generation, testing, debugging, and operations.
• Design engineering workflows where agents can perform bounded development and operational tasks.
• Identify opportunities where AI can improve engineering throughput, quality, or system operations.
• Lead multidisciplinary teams and set engineering standards.
• Facilitate architecture and engineering sessions with senior client technologists.
• Communicate technical trade-offs, risks, and recommendations to both technical and executive audiences.
• Convert lessons learned from delivery into reusable components and practices.

Requirements

• Senior engineer capable of building and operating an entire production system.
• Experience working across application and product engineering, APIs, services, data, cloud infrastructure, DevSecOps, security, observability, and AI.
• Accountable for the end-to-end technical solution from architecture through production.
• Credible in designing distributed systems, provisioning infrastructure, building shipping pipelines, and designing AI and agentic capabilities.
• Experience engineering with AI, demonstrating its measurable impact on delivery.
• Ability to lead multidisciplinary teams and set technical direction.
• Hands-on capability to inspect, challenge, prototype, and debug implementations.
• Experience partnering with client technology leaders.
• Ability to turn ambiguous requirements into architectures, executable increments, and measurable outcomes.
• Experience building net-new platforms and integrating them into existing client estates.
• Experience with containers, Kubernetes, serverless, managed cloud services, and infrastructure as code (Terraform, Helm, or equivalent).
• Experience with cloud networking, compute, storage, identity, secrets, service communication, and workload isolation on AWS, Azure, or GCP.
• Experience building automated CI/CD pipelines, automated testing, security scanning, artifact management, and release controls.
• Experience applying secure-by-design practices.
• Experience with logging, metrics, tracing, and SLOs.
• Experience leading incident response and root-cause analysis.
• Experience designing and implementing transactional, streaming, analytical, and unstructured data flows.
• Experience designing data and context architecture for AI systems.
• Experience designing and implementing AI capabilities using frontier and enterprise models.
• Experience building agentic systems.
• Experience developing evaluation frameworks and AI observability.
• Experience applying AI-native development techniques throughout the lifecycle.
• Experience designing engineering workflows for agents.
• Experience identifying where AI improves engineering throughput, quality, or operations.
• Experience establishing context, tools, permissions, evaluations, and human controls for safe AI integration.
• Experience leading multidisciplinary teams.
• Experience setting engineering standards.
• Experience facilitating architecture and engineering sessions with senior client technologists.
• Experience communicating trade-offs, risks, and recommendations to technical and executive audiences.