Senior AI Engineer

About the Role

We are looking for a Senior AI Engineer with an AI-first engineering mindset to build production-grade Agentic AI capabilities for IZO™+ MCN , our multi-cloud networking platform across AWS, Azure, GCP and OCI.

You will combine AI/LLM engineering, backend development and cloud-native technologies to build intelligent capabilities for network troubleshooting, operational insights, automation and governed remediation.

This is not a prompt-engineering role. You will take AI capabilities from concept → agent → tools/MCP → MCN APIs → production .

What You'll Do

• Design and build Agentic AI solutions using LLMs, tool/function calling, planning, workflows, memory and human-in-the-loop controls.
• Build MCP servers and secure AI tools exposing MCN APIs, cloud inventory, network diagnostics and product knowledge.
• Develop production services using Python and Java/Spring Boot or Go , integrating REST APIs, Kafka and event-driven workflows.
• Build RAG/knowledge solutions using embeddings, vector databases and retrieval optimization.
• Deploy and operate AI services on Docker/Kubernetes with CI/CD.
• Build AI evaluation and observability capabilities covering accuracy, tool-call correctness, latency, reliability and cost .
• Implement AI security and governance , including prompt-injection protection, validation, least-privilege access, auditability and approval controls.
• Use AI throughout the engineering lifecycle for design, development, testing, debugging and productivity .
• Work with Product, Architecture, Engineering, QA, SRE and Security teams to deliver production-ready AI capabilities.

Must Have

• 5–8 years of professional software engineering experience.
• Strong Python development skills; FastAPI, async programming, Pydantic and pytest.
• Strong production experience in Java/Spring Boot or Go .
• Hands-on experience building LLM/GenAI applications using OpenAI, Anthropic, Gemini, AWS Bedrock, Azure AI Foundry, Vertex AI or equivalent.
• Proven experience building Agentic AI systems beyond simple prompt/response applications , including tool calling, multi-step workflows, state/memory and error handling.
• Hands-on experience with MCP and at least one agent framework such as LangGraph, kagent, CrewAI, AutoGen or equivalent .
• Hands-on experience with RAG, embeddings, vector databases and retrieval optimization .
• Experience with AI evaluation and observability , using technologies such as Langfuse, LangSmith, Ragas, DeepEval and/or OpenTelemetry .
• Strong understanding of AI security and responsible AI , including prompt-injection protection, guardrails, validation, least-privilege access and human approval mechanisms.
• Experience with Kafka and event-driven architectures.
• Experience with Docker, Kubernetes and CI/CD .
• Production experience with at least one major public cloud — AWS, Azure, GCP or OCI .
• Strong software engineering fundamentals covering architecture, APIs, testing, debugging and production ownership.
• Demonstrated use of AI-assisted engineering for development, testing, debugging, documentation or productivity.

Good to Have

• Experience with multiple cloud platforms: AWS, Azure, GCP and OCI .
• Cloud networking: VPC/VNet, routing, DNS, NAT, BGP, IPsec, WireGuard .
• AWS Transit Gateway, Azure Virtual WAN, GCP Network Connectivity Center or OCI DRG .
• Dapr, Crossplane, Argo Events or Envoy .
• AIOps/NetOps, anomaly detection or automated remediation.
• Experience with Langfuse/LangSmith/Ragas/DeepEval/OpenTelemetry beyond the core AI evaluation requirement.
• Open-source contributions or cloud/AI certifications.

What Makes This Role Different

You will not just build AI applications . You will help build an AI-native multi-cloud networking platform where AI understands network context, reasons over operational data, interacts with enterprise tools and performs secure, governed actions in production .