AI Native Software Engineer Senior Analyst

Role Overview

Agent Architecture and Engineering

• Design and build enterprise-ready AI agents with 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 or extend abstraction layers across AI providers including Anthropic, Google, and OpenAI.
• Contribute reusable shared libraries, SDKs, and engineering patterns across client engagements.

Cloud-Native Engineering

• Use Kubernetes, Docker, microservices, serverless, event-driven architectures, CI/CD, and observability stacks to deliver scalable systems.
• Own production deployment, monitoring, and troubleshooting for services.

Domain and Client Work

• Tailor and deploy agentic applications across domains such as finance, healthcare, and retail.
• Work with client subject-matter experts to translate business workflows into agentic solutions.
• Participate in or lead design workshops, proof-of-concepts, and code-with sessions with stakeholders.
• Communicate trade-offs, risks, and recommendations to technical and non-technical audiences.

Measurement and Improvement

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

Knowledge Sharing

• Create reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.
• Contribute to internal communities of practice focused on AI-native and agentic engineering.

Travel

• Travel may be required, with the amount varying from 25% to 75% based on business need and client requirements.

Requirements

• Hands-on experience designing and deploying agentic systems in enterprise environments.
• A strong foundation in building cloud-native solutions and operating modern infrastructure.
• Experience designing resilient, testable, maintainable AI-agent workflows with observability.
• Experience integrating multiple AI providers and developing reusable abstraction layers, SDKs, or libraries.
• Experience with Kubernetes, Docker, microservices, serverless, event-driven architectures, CI/CD, or observability stacks.
• Ability to translate client business workflows into domain-specific agentic solutions.
• Ability to communicate trade-offs, risks, and recommendations to technical and non-technical audiences.
• Experience defining metrics, evaluation plans, and test harnesses for agent quality, safety, latency, and cost.
• Ability to lead complex agentic solutions or own critical technical areas end-to-end as a senior individual contributor.