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.
- Robust 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.

📌 Senior AI Engineer (Bengaluru)
🏢 Tata Communications
📍 Bengaluru