Cloud Enterprise Architect - AI, AWS, Python

TeizoSoft Private Limited; Hyderabad, Chennai, Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

TeizoSoft Private Limited is seeking an Enterprise Architect to lead the technical direction for AI-enabled, cloud-native platforms in production from Hyderabad. Translate business asks into designs and own end-to-end integration architecture, with AWS as the primary cloud and Azure/GCP as required.

The ideal candidate is an integration specialist with deep AWS expertise, fluent across Azure and GCP, and able to align executives and engineering teams while embedding security, observability, and

Qualifications

• Integration is the core skill: AI, data, cloud, and enterprise systems must work together in production.
• Production-depth experience with Python and FastAPI, including service design and code review.
• Hands-on Terraform, Docker, Kubernetes, and CI/CD in real delivery contexts.

Responsibilities

• Translate business asks into technical designs and align stakeholders.
• Own end-to-end integration architecture, including API contracts, data flow, and observability.
• Build internal developer platforms with access tiers, authentication, gateway config, and quota management.
• Define and evolve integration patterns and reusable assets for engineering teams.
• Review frontend and infra delivery with React, Terraform, and CI/CD to catch issues early.

Skills

AWS

Azure

GCP

Python

FastAPI

Terraform

Docker

Kubernetes

CI/CD

Security

Observability

React

Leadership

API design

Tools

Bedrock

Pinecone

FAISS

Weaviate

Qdrant

Kafka

OAuth

Okta

Job description

ENTERPRISE ARCHITECT

Technology Solutions and Cloud Architecture

Hyderabad-based, Hybrid working, Full-time

ABOUT THE ROLE

We are looking for an Enterprise Architect to lead the technical direction for AI-enabled, cloud-native platforms in production. You will translate business asks into buildable designs, own integration architecture end to end, and set the patterns that other engineering teams build on. AWS is the primary cloud environment, and working knowledge of Azure and GCP is also required.

THE IDEAL CANDIDATE IS

• An integration specialist by trade who has spent years making AI, data, cloud, and enterprise systems work together in production, and knows where they tend to break.
• Technically deep in AWS, with honest working fluency across Azure and GCP.
• Fluent in GenAI and data platform patterns, with the depth to make architecture decisions that hold up over time even without a data science background.
• Comfortable moving between a deep engineering design session and an executive briefing without losing precision in either.
• Disciplined about security, observability, and failure handling from the first draft of a design rather than later.

WHAT YOU WILL DO

Architecture and technical direction

• Translate business asks (usually a one-liner) into technical designs, align senior stakeholders on the direction, and give engineering teams a clear brief they can build from.
• Own the integration architecture end to end, including API contracts, data flow, failure modes, retry behaviour, and observability, maintained through to production.
• Build the internal developer platforms that other teams build on, including access tiers, OAuth and Okta authentication, gateway configuration, and quota management.
• Define and evolve the integration patterns and reusable assets that other engineering teams adopt.
• Guide frontend and infrastructure delivery with enough React, Terraform, and CI/CD knowledge to review work and flag architectural issues before they reach production.

AI and data platform integration

• Integrate the AI stack into production systems using FastAPI orchestration across Bedrock, vector search, document pipelines, and analytics, with the API contracts and runbooks to support it.
• Understand the AI stack well enough to make integration decisions that hold over time. LLM lifecycles, embedding pipelines, RAG, multi-agent patterns and inference latency are all things you need to reason through confidently.
• Choose the right vector database for each use case across Pinecone, FAISS, Weaviate, and Qdrant, with a clear rationale for each choice.
• Integrate AI capabilities into production platforms end to end, covering ingestion, enrichment, post-processing, observability, and failure handling.
• Design responsible AI safeguards into the platform from the start, including monitoring pipelines, bias detection, and explainability. Should have a clear point of view on how to do this and why.

Cloud infrastructure and resilience

• Design event-driven pipelines on AWS using Lambda, Step Functions, EventBridge, and Kafka, with dead letter queues, retries, structured logging, and distributed tracing built in from the start.
• Know the AWS service landscape well. Choose the right AWS service for each problem across Lambda, EKS, Bedrock, SageMaker, Step Functions, EventBridge, IAM, and VPC, with clear awareness of the cost and operational trade-offs of each choice.
• Ship infrastructure through Terraform, Docker, Kubernetes, and CI/CD.
• Build security and observability into the platform from the start as first principles, including OAuth, SSO, zero-trust design, structured logging, and distributed tracing.

Production ownership

• Lead production incidents end to end, covering triage, root cause analysis, fixes, and runbook documentation.
• Drive cross-team issues to resolution when they span network, infrastructure, product, or business teams.

WHAT WE ARE LOOKING FOR

Experience and technical depth

• 10-15 years in engineering, with substantial time in architecture or principal-level technical leadership roles. Someone who has had real ownership of how systems are designed and delivered.
• Integration is the core skill here. Years of making AI, data, cloud, and enterprise systems work together reliably, with a genuine understanding of where they fail.
• Production-depth experience with Python and FastAPI, including service design and code review.
• Depth across AWS services including Lambda, EKS, Bedrock, SageMaker, Step Functions, EventBridge, IAM, and VPC, with awareness of the cost and operational implications of each choice.
• Hands-on experience shipping infrastructure through Terraform, Docker, Kubernetes, and CI/CD as part of actual delivery.
• A track record of treating security and observability as design defaults, covering OAuth, SSO, zero-trust, structured logging, and distributed tracing.
• Enough React knowledge to review a codebase and guide delivery. Writing production frontend code is not required.
• Hands-on experience with Kafka or other production streaming platforms for event-driven architectures.
• Experience designing developer-facing API platforms at scale, including access tiers, gateway configuration, and quota controls.

Multi-cloud fluency

• Working knowledge of Azure across Azure OpenAI Service, AKS, API Management, Entra ID, and Data Factory for enterprise integration scenarios.
• Working knowledge of GCP across Vertex AI, BigQuery, Cloud Run, Pub/Sub, and Apigee.
• A clear understanding of the cost and operational trade-offs across cloud providers.

AI and GenAI

• Working fluency across LLM lifecycles, embedding pipelines, RAG, multi-agent patterns, and inference latency. A data science background is not required.
• Familiarity with vector databases including Pinecone, FAISS, Weaviate, and Qdrant, with a clear sense of when to use each.
• Experience integrating AI systems into production platforms end to end, covering ingestion, enrichment, post-processing, observability, and failure modes.
• A clear point of view on responsible AI as an architecture concern, including how to design monitoring pipelines, bias detection, and explainability into the platform from the start.

HOW YOU WORK

• You move from ambiguous business asks to well-designed systems without needing the brief tightened first.
• You document API contracts, architecture decisions, and runbooks as you go, because it saves the team time later.
• You set the standard when one is missing. If teams across the business are solving the same problem differently, you create a pattern that holds and that others actually adopt.
• You take a clear position on technical trade-offs and bring stakeholders along, including engineering, product, and executive audiences.

NICE TO HAVE

• Prior experience in a consulting or advisory architecture role across multiple industries or client environments.
• Background in media and entertainment, or other content-heavy industries where ingestion, rights, and metadata complexity are part of the day-to-day.
• Familiarity with MLOps practices covering model versioning, experiment tracking, governance, and controlled deployment of AI models and adapters.
• Experience designing or operating cost and FinOps controls for cloud and AI workloads at scale.
• Experience with identity and access patterns under strict audit or compliance regimes such as SOX, SOC 2, or equivalent.