Principal Cloud Engineer Data & AI

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

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

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

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)

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

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

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

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

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

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.

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