Job Title:  Manager | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering

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

1. 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
• Conduct root cause analysis (RCA) and implement preventive measures

2. 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

3. 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)

4. GPU & High-Performance Compute Optimization

• Manage and optimize GPU/TPU-based workloads
• Ensure efficient:
• Workload scheduling
• 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

5. Observability & Intelligent Monitoring

• Implement end-to-end observability across:
• Infrastructure (CPU, GPU, memory)
• Platform services
• AI models
• Define metrics for:
• Model drift, bias, latency, throughput
• Deploy monitoring tools:
• Prometheus, Grafana, ELK, Azure Monitor, Datadog
• Enable predictive alerting and AIOps capabilities

6. Security, Compliance & Responsible AI

• Ensure secure operation of AI systems:
• Identity & access management (IAM/RBAC)
• Data encryption (at rest & in transit)
• Secure model endpoints
• 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

7. FinOps & Cost Optimization

• Drive cost efficiency for AI workloads:
• GPU and compute optimization
• Storage and data transfer optimization
• Implement:
• Autoscaling and workload scheduling strategies
• Spot/preemptible usage
• Build:
• Cost dashboards and chargeback models
• Align AI infrastructure spend with business outcomes

8. 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

9. Team Leadership & Talent Development

• Lead and mentor a team of:
• MLOps engineers
• Cloud/platform engineers
• SREs / AI Ops specialists
• Responsibilities include:
• Workforce planning and hiring
• Capability development and certifications
• Performance management
• Foster a culture of:
• Automation-first mindset
• Reliability engineering
• DevOps practices

10. Stakeholder & Program Management

• Partner with:
• Data science and AI engineering teams
• Enterprise architects
• Security and governance teams
• Translate business requirements into:
• Scalable AI infrastructure solutions
• Provide leadership updates on:
• Platform health
• Cost metrics
• Operational KPIs

11. 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:
• Automation
• Standardization
• Reliability

Required Qualifications

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Experience

• 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)

Technical Skills

Cloud & Infrastructure

• Azure, AWS, GCP (multi-cloud preferred)
• Kubernetes, Docker
• Infrastructure as Code (Terraform, ARM/Bicep, CloudFormation)

AI/ML & MLOps

• Platforms: Azure ML, SageMaker, Vertex AI, MLflow, Kubeflow
• Model lifecycle management and pipeline orchestration

Data & Processing

• Apache Spark, Kafka, Airflow
• Data pipelines and feature stores

Observability & Monitoring

• Prometheus, Grafana, ELK stack, Datadog

Programming

• 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

Preferred Qualifications

• Certifications:
• Azure/AWS/GCP Architect
• 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