Sr Principal Infrastr Eng

Job Description

Role: AI Platform Consultant - Build

Location: Hyderabad

Who are we looking for?

A senior AI Platform Consultant having 10-12 years of overall IT and cloud experience, with strong hands-on expertise in building and enabling enterprise AI platform services on Azure and AWS. The individual should be capable of implementing approved designs for Azure OpenAI and related Azure AI services, Amazon Bedrock, and cloud machine learning platform components without assuming responsibility for application development or business use-case delivery. The candidate should understand AI platform access, model deployment, private connectivity, identity, guardrails, data integration, developer enablement, automation, and operational readiness and should coordinate effectively with cloud, security, network, data, observability, and application teams.

Key Responsibilities:

• Understand approved Azure and AWS AI platform designs, service selections, access models, deployment standards, and developer-enablement requirements and translate them into implementation activities.
• Provision and configure Azure OpenAI and related Azure AI platform resources, model deployments, capacity settings, endpoints, managed identities, and approved access controls.
• Enable Azure AI Search, approved vector search capabilities, content-safety controls, private access, and platform connections required for enterprise AI experimentation and development.
• Provision and configure Amazon Bedrock model access, inference configurations, Knowledge Bases, Agents, Guardrails, and approved supporting services based on the defined solution design.
• Configure approved Amazon SageMaker platform capabilities such as domains, user profiles, execution roles, workspaces, model endpoints, and shared development settings where required.
• Implement AI platform identity and access configurations using Azure RBAC, managed identities, AWS IAM roles, service principals, and secure secrets references in coordination with security teams.
• Coordinate private endpoints, VPC or VNet connectivity, DNS, firewall access, service endpoints, and data-path requirements with cloud network engineers.
• Configure content filters, Bedrock Guardrails, model access restrictions, logging, audit settings, quota controls, and other approved baseline governance measures.
• Set up developer-facing platform access, service connections, project workspaces, approved SDK or API connectivity, and baseline examples required to confirm service usability.
• Develop or support Terraform, Bicep, CloudFormation, and pipeline-based deployment assets for repeatable AI platform provisioning and configuration.
• Perform platform readiness checks including model invocation, endpoint accessibility, identity validation, private connectivity, logging visibility, quota availability, and basic service integration tests.
• Troubleshoot AI platform provisioning, model access, permission, quota, network, endpoint, SDK, and service integration issues during implementation.
• Prepare build documents, configuration references, access procedures, platform usage guidance, readiness evidence, known limitations, and knowledge-transfer material.

Technical Experience:

• Strong hands-on experience implementing enterprise AI and machine learning platform services on Microsoft Azure and Amazon Web Services.
• Good experience with Azure OpenAI, Azure AI services, Azure AI Search, Azure Machine Learning, model deployment, private endpoints, managed identities, and developer access configuration.
• Good experience with Amazon Bedrock including foundation model access, inference, Knowledge Bases, Agents, Guardrails, IAM integration, and supporting AWS services.
• Working knowledge of Amazon SageMaker platform components including domains, user profiles, Studio environments, execution roles, model endpoints, and model registry concepts.
• Good understanding of retrieval-augmented generation platform components including embedding models, vector indexes, knowledge sources, chunking, access controls, and data refresh dependencies.
• Strong understanding of AI platform security and governance including identity, least-privilege access, content safety, guardrails, encryption, private networking, secrets, and audit logging.
• Good understanding of Infrastructure as Code and CI/CD deployment using Terraform, Bicep, CloudFormation, Git, Azure DevOps, GitHub Actions, or equivalent tools.
• Working knowledge of AI platform observability, token or request metrics, logging, model endpoint health, quota tracking, cost visibility, and operational readiness considerations.
• Strong troubleshooting and documentation skills with the ability to resolve cross-service configuration issues and provide clear build and enablement guidance.

Process Skills:

• Good experience in controlled cloud platform implementation, technical validation, change management, release procedures, and environment promotion processes.
• Ability to coordinate technical dependencies across cloud platform, network, security, data, observability, application, and automation teams.
• Strong documentation discipline covering build procedures, access models, configuration records, readiness results, issue logs, and knowledge transfer.
• Understanding of responsible AI, model governance, data privacy, and approval processes applicable to enterprise AI platform enablement.

Behavioral Skills:

• Excellent communication skills with the ability to explain AI platform dependencies and limitations to architects, engineers, and client stakeholders.
• Strong problem-solving skills with the ability to troubleshoot identity, network, model, quota, and service integration issues methodically.
• Ability to work collaboratively across cloud, application, data, security, and vendor teams without expanding into application development ownership.
• High ownership for build quality, platform readiness, documentation, and timely escalation of unresolved service dependencies.

Certification:

• Graduate or equivalent degree in computer science, engineering, information technology, data science, or a related field.
• Azure AI Engineer Associate or a relevant AWS AI certification is strongly preferred.
• Azure, AWS, or HashiCorp Terraform architecture certification is an added advantage.