AI Native Software Engineering Specialist

In this role you design, build, and deploy production-grade agentic AI systems at scale for enterprise clients. You’ll work closely with client engineering teams, lead technical design sessions, and create reusable patterns to accelerate future engagements. The role sits at the intersection of AI engineering and client delivery, with a focus on practical, production-ready solutions. You will influence architecture choices and drive measurable impact across deployments.

• Design and build end-to-end production-grade agentic systems (multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, lifecycle observability)
• Own RAG pipelines (embeddings, chunking, vector search, context engineering)
• Integrate across OpenAI, Anthropic, Vertex AI and open-source models with fallback routing and cost/latency management
• Implement LLMOps in production (eval harnesses, prompt versioning, observability tooling, cost and safety monitoring)
• Collaborate with client teams through workshops, proofs of concept, and architecture walkthroughs
• Create reusable patterns, accelerators, and playbooks to scale engagements
• Define and report metrics on agent accuracy, latency, safety, and cost-effectiveness to stakeholders

• Minimum 1 year designing and deploying agentic AI in production
• Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) at production depth
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code
• RAG pipeline ownership (embeddings, chunking, vector databases, context engineering)
• LLMOps fundamentals (eval harness design, prompt versioning, production observability)
• Cloud-native engineering maturity (Kubernetes, Docker, microservices, CI/CD, IaC with Terraform or Helm)
• Strong Python; Java or equivalent backend language; production debugging and observability
• Production-shipped experience preferred over generalist exposure

• client collaboration and workshop facilitation
• ability to translate technical findings into business terms
• problem solving under production pressure
• agentic orchestration frameworks (LangGraph, CrewAI, AutoGen)
• LLM APIs (OpenAI, Anthropic, Vertex AI)
• RAG pipelines and vector databases