AI Native Software Engineer

Overview

The Work

Agent Architecture & Engineering

• Design and build enterprise-ready AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Implement resilient, testable, and maintainable agentic workflows that can be iterated on quickly.

AI Platform Integration

• Develop and/or extend abstraction layers across AI providers (Anthropic, Google, OpenAI, etc.) to enable seamless integration and multi-provider enablement.
• Contribute to shared libraries, SDKs, and patterns that can be reused across clients.

Cloud-Native Engineering

• 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 for your services in production.

Domain-Specific Workflows

• Tailor and deploy agentic applications across verticals (e.g., finance, healthcare, retail), adapting to domain-specific processes and constraints.
• Work closely with client SMEs to translate business workflows into agentic solutions.

Client Engagement

• Participate in and/or lead design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption.
• Communicate trade-offs, risks, and recommendations clearly to both technical and non-technical audiences.

Measure & Improve

• Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
• Iterate rapidly based on data, feedback, and changing requirements.

Knowledge Sharing

• Craft reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
• Contribute to internal communities of practice around AI-native and agentic engineering.
• Travel may be required for this role.
• The amount of travel will vary from 25% to 75% depending on business need and client requirements.

Here’s What You Need

• Minimum of 3 years of engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
• Minimum of 1 year of hands-on experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production or near-production environments.
• Minimum of 1 year of experience with modern AI platforms — OpenAI, Claude, Vertex AI, or open-source models — including building or using abstraction layers for multi-provider pipelines.
• Minimum of 3 years strong Python, Java or equivalent experience building 12 factor applications + Infrastructure as Code (Terraform, Helm)
• Minimum of 3 years of experience in client-facing communication and collaboration, including leading technical discussions, workshops, or delivery sessions under ambiguity.
• Bachelor's degree in Computer Science, Engineering or equivalent OR equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)

Bonus Points If You Have

• Relevant AI certifications or agentic tooling experience are a plus.
• You’ve served as an Agentic / AI Engineer in an enterprise environment.
• You’ve built multi agent orchestrations using (Lang-graph, Crew AI, Claude SDK, Open AI SDK, etc).
• Have a GitHub repo with an agent/plugins you have created
• You have additional AI certifications or experience with agentic tooling and frameworks.
• You’ve defined or worked with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry / stream-based architectures.
• You understand the AI-native paradigm — blending cloud-native with generative model architectures — optimizing for performance, modularity, and efficiency.
• You’ve delivered solutions across multiple industries (e.g., finance, healthcare) by tailoring agentic workflows to industry needs.
• Driven execution across multiple workstreams, ensuring quality, delivery, and alignment with client outcomes.