AI Infrastructure Architect

1 day ago

Anand, Gujarat, India beBeeInfrastructure Full time ₹ 1,80,00,000 - ₹ 2,50,00,000
Senior AI Infrastructure Engineer">

Maintain scalable, efficient, and secure AI infrastructure for high-performance workloads.

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Design and implement end-to-end ML pipelines (training, evaluation, deployment) for LLMs, CV, and multimodal models.

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Lead efforts in model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows.

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Build and optimize GPU-backed serving infrastructure targeting p99 latency < 100 ms, 99.9% uptime, and > 80% GPU utilization.

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Drive initiatives on model governance, automated drift detection (≤10% false positives), and data-management best practices.

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Integrate vector databases (Qdrant, Pinecone) for low-latency semantic retrieval, and build agentic workflows using LangChain or similar frameworks.

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Architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations.

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Design Prometheus/Grafana dashboards, ELK/Fluentd logging pipelines, and alerting for all ML workloads.

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Maintain CI/CD pipelines for Python (FastAPI) and TypeScript (NestJS) inference services.

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Define and track SLAs/SLOs, optimize cloud spend by ≥ 20% year-over-year, and ensure GPU clusters operate at > 80% utilization.

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Partner with AI researchers, product managers, and legal to align MLOps standards with compliance and roadmap goals.

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Mentor junior engineers, run quarterly brown-bags, own onboarding docs (upskill 5+ engineers/quarter), and publish ≥ 1 open-source contribution or talk annually.

Requirements:
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9-14 years in software engineering, including ≥ 4 years in MLOps or ML infrastructure.

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Strong expertise in cloud platforms (AWS/GCP/Azure), Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow.

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Experience with inference frameworks (Triton, TensorFlow Serving, BentoML, TorchServe).

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Familiarity with distributed training, workload schedulers, and GPU-cluster orchestration.

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Proficiency in Python, TypeScript, and infrastructure-as-code (Terraform, Helm, etc.).

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Proven track record building reliable, scalable ML systems in production.

Critical Skills:
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Vector DB integration (Qdrant, Pinecone).

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Agent orchestration (LangChain, LlamaIndex).

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Multi-tenant security and RBAC.

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Observability stacks (Prometheus/Grafana, ELK).

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CI/CD for FastAPI/NestJS services.

Preferred:
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Master's/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD.

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Prior experience at AI-focused startups or enterprises scaling ML for 100–500 orgs.

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Understanding of low-latency streaming inference or agent-based LLM systems.

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Excellent written and verbal communication, and a proven ability to drive consensus across functions.

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