Role Overview:

Architects, deploys, and scales production-grade Generative AI applications and autonomous agents. Bridges AI modeling and distributed systems engineering using cloud-native patterns, microservices, and Kubernetes.

Key Responsibilities

- Model Serving &
- Kubernetes:

Deploy open-weights models (vLLM, TensorRT-LLM) on Kubernetes (EKS/GKE/AKS) with GPU autoscaling and latency optimization.

- Vector &

- RAG Pipelines:

Build high-throughput retrieval architectures using vector databases (Qdrant, Milvus, Pinecone, pgvector) and streaming data pipelines.

- Agentic Orchestration: Develop multi-agent workflows (LangGraph, CrewAI) and integrate external tool calling protocols (MCP).

- LLMOps &

- Governance:

Set up tracing (LangSmith, OpenTelemetry), guardrails (NeMo Guardrails), and FinOps token budget controls.

Core Skill Requirements

- Infrastructure: Kubernetes, Docker, Terraform, AWS/GCP/Azure.

- Languages &
- Frameworks:

Python (FastAPI), Go or TypeScript, LangChain/LangGraph, LlamaIndex, PyTorch/Hugging Face.

- Data &

- Inference:

Vector DBs, vLLM/Ollama, Kafka, PostgreSQL.

📌 Cloud Native GenAI Engineer - Technical Architect (Bengaluru)
🏢 Virtusa
📍 Bengaluru