Principal Cloud Engineer - Data & AI

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 te