Principal Cloud Engineer

Job Summary

• Were looking for a Principal Cloud Engineer with a strong foundation in Multi-Cloud & multi region deployment, data architecture, distributed systems, and modern cloud-native platforms to architect, build, and maintain intelligent infrastructure and systems that power our AI, GenAI and data-intensive workloads.
• Youll work closely with cross-functional teams, including data scientists, ML & software engineers, and product managers & play a key role in designing a highly scalable platform to manage the lifecycle of data pipelines, APIs, real-time streaming, and agentic GenAI workflows, while enabling federated data architectures.
• The ideal candidate will have a strong background in building and maintaining scalable AI & Data Platform, optimizing workflows, and ensuring the reliability and performance of Data Platform systems.

Responsibilities

Cloud Architecture & Engineering

• Deep expertise in designing, implementing, and managing architectures across multiple cloud platforms (e.g., AWS, Azure, GCP)
• Proven experience in architecting hybrid and multi-cloud solutions, including interconnectivity, security, workload placement, and DR strategies
• Strong knowledge of cloud-native services (e.g., serverless, containers, managed databases, storage, networking)
• Experience with enterprise-grade IAM, security controls, and compliance frameworks across cloud environments

AI & GenAI Platform Integration

• Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experience
• Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOps
• Experience in developing and integrating GenAI applications using MCP and orchestration of LLM-powered workflows (e.g., summarization, document Q&A, chatbot assistants, and intelligent data exploration)
• Hands-on expertise building and optimizing vector search and RAG pipelines using tools like Weaviate, Pinecone, or FAISS to support embedding-based retrieval and real-time semantic search across structured and unstructured datasets

Engineering Enablement

• Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practices
• Streamline onboarding, documentation, and platform implementation & support using GenAI and conversational interfaces
• Collaborate across teams to enforce cost, reliability, and security standards within platform blueprints.
• Work with engineering by introducing platform enhancements, observability, and cost optimization techniques
• Foster a culture of ownership, continuous learning, and innovation

Automation, IaC, CI/CD

• Mastery of Infrastructure as Code (IaC) tools especially Terraform, Terragrunt, and CloudFormation / ARM / Deployment Manager
• Experience building and managing cloud automation frameworks (e.g., using Python, Go, or Bash for orchestration and tooling)
• Hands-on experience with CI/CD pipelines (e.g., GitHub Actions) for cloud resource deployments
• Expertise in implementing policy-as-code & Compliance-as-code (e.g., Open Policy Agent, Sentinel)

Security, Governance & Cost

• Strong background in implementing cloud security best practices (network segmentation, encryption, secrets management, key management, etc.).
• Experience with multi-account / multi-subscription / multi-project governance models, including landing zones, service control policies, and organizational structures
• Ability to design for cost optimization, tagging strategies, and usage monitoring across cloud providers

Monitoring & Operations

• Familiarity with cloud monitoring, logging, and observability tools (e.g., CloudWatch, Azure Monitor, GCP Operations Suite, Datadog, Prometheus)
• Experience with incident management and building self-healing cloud architectures

Platform & Cloud Engineering

• Develop and maintain real-time and batch data pipelines using tools like Airflow, dbt, Dataform, and Dataflow/Spark
• Design and develop event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent messaging systems
• Build and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agents
• Architect and manage multi-cloud and hybrid cloud platforms (e.g., GCP, AWS, Azure) optimized for AI, ML, and real-time data processing workloads
• Build reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI/CD to drive self-service and automation
• Ensure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineering

Leadership & Collaboration

• Experience leading cloud architecture reviews, defining standards, and mentoring engineering teams
• Ability to work cross-functionally with security, networking, application, and data teams to deliver integrated cloud solutions
• Strong communication skills to engage stakeholders at various levels, from engineering to executives

Qualifications

• 15+ years of hands-on experience in Platform or Data Engineering, Cloud Architecture, Multi-Cloud Multi-Region Deployment & Architecture, AI Engineering roles
• Strong programming background in Java, Python, SQL, and one or more general-purpose languages
• Deep knowledge of data modeling, distributed systems, and API design in production environments
• Proficiency in designing and managing Kubernetes, serverless workloads, and streaming systems (Kafka, Pub/Sub, Flink, Spark)
• Experience with metadata management, data catalogs, data quality enforcement, and semantic modeling & automated integration with Data Platform
• Proven experience building scalable, efficient data pipelines for structured and unstructured data
• Experience with GenAI/LLM frameworks and tools for orchestration and workflow automation
• Experience with RAG pipelines, vector databases, and embedding-based search
• Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stack
• Experience with ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI/ML observability tools
• Prior implementation of data mesh or data fabric in a large-scale enterprise
• Experience with Looker Modeler, LookML, or semantic modeling layers

Preferred Certifications

• AWS Certified Solutions Architect Professional
• Google Professional Cloud Architect
• Microsoft Certified: Azure Solutions Architect Expert
• HashiCorp Certified: Terraform Associate
• Other relevant certifications (CKA, CKS, CISSP cloud concentration) are a plus.

Why Youll Love This Role

• Drive technical leadership across AI-native data platforms, automation systems, and self-service tools
• Collaborate across teams to shape the next generation of intelligent platforms in the enterprise
• Work with a high-energy, mission-driven team that embraces innovation, open-source, and experimentation