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Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering at Deloitte South Asia · Hyriko
via Career pages · 3w ago
Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering Deloitte South Asia
Full-time Hybrid
Location: Bengaluru, India Type: Full-time Posted: 3w ago
Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering
Job requisition ID : 107230 Location: Bengaluru Entity: Deloitte Touche Tohmatsu India LLP Job Title: AI Infrastructure Architect / Operate Lead (Manager)
Role Summary
The AI Infrastructure Architect / Operate Lead is responsible for operationalizing, managing, and optimizing AI/ML platforms and infrastructure at scale. This role focuses on ensuring high availability, reliability, performance, security, and cost efficiency of AI workloads across multi-cloud and hybrid environments.
The role bridges AI engineering, cloud platform operations, MLOps, DevOps, and SRE practices, enabling organizations to run production-grade AI systems with strong governance and operational excellence.
Key Responsibilities AI Platform Operations & Service Reliability Own end-to-end operations of AI platforms and infrastructure, including: Model serving platforms (batch & real-time) AI pipelines and orchestration frameworks Data ingestion and processing layers - Ensure: 99.9%+ availability and resilience Defined SLOs/SLIs for AI services Lead incident, problem, and change management processes
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Conduct root cause analysis (RCA) and implement preventive measures MLOps & Model Lifecycle Management Lead operationalization of end-to-end ML lifecycle: Model training, validation, deployment, monitoring, retraining
- Implement and manage: ML pipelines (CI/CD for models) Model registry and versioning
- Ensure: Model reproducibility and traceability Model performance tracking (latency, accuracy) Drift detection (data drift / concept drift) Integrate automated retraining and feedback loops Cloud & Platform Engineering Oversee deployment and operations across Azure, AWS, GCP, and hybrid environments
- Manage: Kubernetes clusters (On-prem/AKS/EKS/GKE) Serverless and container-based AI workloads
- Drive: Infrastructure-as-Code (IaC) adoption (Terraform, Bicep, CloudFormation) Platform standardization and reusable components Ensure scalable infrastructure for training (high compute) and inference (low latency) GPU & High-Performance Compute Optimization Manage and optimize GPU/TPU-based workloads
- Ensure efficient: Resource allocation and bin-packing
- Optimize infrastructure for: Distributed training (e.g., Horovod, DeepSpeed) Cost-performance trade-offs Monitor GPU utilization and improve efficiency metrics Observability & Intelligent Monitoring
- Implement end-to-end observability across: Infrastructure (CPU, GPU, memory)
- Define metrics for: Model drift, bias, latency, throughput
- Deploy monitoring tools: Prometheus, Grafana, ELK, Azure Monitor, Datadog Enable predictive alerting and AIOps capabilities Security, Compliance & Responsible AI Ensure secure operation of AI systems: Identity & access management (IAM/RBAC) Data encryption (at rest & in transit)
- Enforce: Data privacy regulations (GDPR, HIPAA, etc.) Responsible AI policies (bias detection, explainability)
- Maintain: Audit trails for models and data Governance frameworks for model lifecycle FinOps & Cost Optimization Drive cost efficiency for AI workloads: GPU and compute optimization Storage and data transfer optimization
- Implement: Autoscaling and workload scheduling strategies
- Build: Cost dashboards and chargeback models Align AI infrastructure spend with business outcomes Service Delivery & Operations Management Lead 24x7 operations support (if applicable) Manage SLAs, OLAs, and KPIs
- Implement ITIL-based processes: Incident, problem, change, release management Drive continuous service improvement initiatives Team Leadership & Talent Development Lead and mentor a team of: SREs / AI Ops specialists
- Responsibilities include: Workforce planning and hiring Capability development and certifications
- Foster a culture of: Stakeholder & Program Management
- Partner with: Data science and AI engineering teams Security and governance teams
- Translate business requirements into: Scalable AI infrastructure solutions
- Provide leadership updates on: Continuous Improvement & Innovation
- Introduce: Self-healing infrastructure Autonomous operations using AI (AIOps)
- Evaluate new technologies: LLMOps (vector DBs, prompt pipelines, inference optimization) Edge AI and distributed inference
- Improve platform maturity across:
Education Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
- 12+ years in: Cloud/platform engineering or infrastructure operations At least 3-5 years in AI/ML infrastructure or MLOps Proven team management experience (Manager level) Azure, AWS, GCP (multi-cloud preferred) Infrastructure as Code (Terraform, ARM/Bicep, CloudFormation) Platforms: Azure ML, SageMaker, Vertex AI, MLflow, Kubeflow Model lifecycle management and pipeline orchestration Apache Spark, Kafka, Airflow Data pipelines and feature stores Observability & Monitoring
Prometheus, Grafana, ELK stack, Datadog Python, Bash, or scripting languages Leadership & Functional Skills
Strong people leadership and delivery management Experience in SRE / DevOps transformations Knowledge of ITIL-based service management Strong stakeholder communication and executive reporting
- Certifications: Certified Kubernetes Administrator (CKA) AI/ML certifications (Azure ML, AWS ML Specialty)
- Experience with: Generative AI / LLMOps ecosystems Vector databases (FAISS, Pinecone, etc.) Responsible AI frameworks
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