Job Description
Seeking a hands-on AI Native Software Engineer to design, build, and deploy production-grade AI-driven systems within enterprise environments. The role focuses on implementing agent-based workflows, integrating AI platforms, and delivering scalable cloud-native solutions.
Responsibilities
- AI Agent Engineering
- Design and implement AI agents, including:
◦ Retrieval (RAG)
◦ Orchestration workflows
◦ Tool/function invocation
- ◦ Policy-based routing
- Build evaluation frameworks for accuracy, latency, and reliability
- Implement observability and monitoring for agent lifecycle.
- AI Platform Integration
- Integrate with AI providers (e.g., OpenAI, Anthropic, Google Vertex, open-source models)
- Build abstraction layers to support multi-model and multi-provider architectures
- Optimize model usage for performance, cost, and latency
- Cloud-Native Development
- Develop scalable services using:
◦ Microservices architecture
◦ Containers (Docker, Kubernetes)
- ◦ Serverless and event-driven patterns
- Implement CI/CD pipelines and infrastructure as code (e.g., Terraform, Helm)
- Ensure production readiness, logging, monitoring, and fault tolerance
- Application Development
- Build and deploy AI-powered applications aligned to business workflows
- Integrate AI systems into existing enterprise platforms and APIs
- Develop backend services and APIs supporting agent workflows
- Testing & Performance
- Define and execute test strategies for AI systems
- Measure system performance (latency, throughput, accuracy, cost)
- Debug and optimize production systems.
- Required Skills & Experience
- Strong experience with cloud-native systems (APIs, microservices, containers, serverless)
- Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration)
- Proficiency in Python, Java, or similar backend languages
- Experience with:
◦ CI/CD pipelines
◦ Infrastructure as code
- ◦ Monitoring and observability tools
- Hands-on experience with AI platforms (OpenAI, Claude, Vertex AI, or similar)
- Preferred Experience
- Experience with agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
- Experience designing multi-agent or distributed AI systems
- Familiarity with enterprise-scale system integration
- Experience optimizing AI workloads for cost and performance.
- Scope & Expectations
- 100% hands-on engineering role (no people management)
- Deliver production-quality code and deployments
- Work within existing architecture and engineering standards
- Collaborate with client and internal engineering teams as needed
- Participate in technical design discussions (implementation-focused)
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