Sr. AI Engineer
Hybrid (Atlanta, Austin, Boston, Charlotte, Chicago, Cinccinati,Cleveland, Columbus, Dallas, Denver,Detroit, Hartford,Houston,Indianapolis, Irvine,Kansas City, LA, Miami, Milwaukee,Minneapolis, New York,Philadlphia, Pheonix, Pittsburg,Raleigh,San Antonio,Seattle,St. Louis,Tampa, Washington DC )
Long Term
Overview
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
- 10+ years of software engineering 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 multiple 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 (Contractor-Specific)
- 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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