Architect - AppDev
New Delhi, India
R
INR 2535K-4500K (estimate) Senior-level Full Time
Tasks
- Apply LoRA QLoRA fine tuning
- Apply RAG and prompt engineering
- Apply quantization for LLM optimization
- Awareness of GPU Operator CUDA device plugins and MIG
- Build and deploy AI applications
- Build and manage container images and registries
- Build end-to-end ML pipelines
- Deploy and manage AI workloads on OpenShift AI
- Deploy and optimize model serving
- Design and deploy AI agents
- Design production-grade AI systems
- Implement LLMOps practices
- Implement Model Context Protocol server integrations
- Implement RBAC and SCCs security
- Implement agent to agent communication protocols
- Implement model evaluation and prompt workflows
- Integrate tools and multi step workflows
- Manage Kubernetes networking ingress and routes
- Manage Kubernetes pods deployments services and config maps
- Manage real time and batch AI workloads
- Manage storage PVCs
- Monitor latency TPS TTFT and cost per request
- Use Podman and Docker for containers
- Use TensorRT and Triton Inference Server
Perks/Benefits
Skills/Tech-stack
A2A | CUDA | ConfigMaps | Docker | GPU Operator | Grafana | Inference Server | Ingress | Kubeflow Pipelines | Kubernetes | Kubernetes RBAC | LLMOps | LLama Stack | Langchain | Langgraph | Llama Agents | LoRA | MLOps | Model Context Protocol | NVIDIA GPU | NVIDIA GPU Operator | OpenShift | Persistent Volume | Persistent Volume Claims | Podman | Prometheus | QLoRA | Quantization | RAG | Secrets | TensorRT | Triton Inference | Triton Inference Server | VLLM
Education
N/A
Roles
AI | AI Architect | AI Technical Lead | Architect | Lead | Senior AI Technical Lead | Technical Lead
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