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Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning & GenAI solutions—spanning predictive analytics, optimization, NLP, and AI-driven platforms—that help leading enterprises forecast, automate, and gain a competitive edge.

Behind these innovations lies robust, secure, and scalable cloud infrastructure. As part of our Cloud Engineering team, you’ll help design and operate next-gen cloud systems that power cutting-edge AI solutions.

Job Overview

We seek a Senior Cloud Engineer to design, build, and optimize scalable, secure, and cost-efficient cloud environments. You’ll collaborate with AI/ML teams to deliver production-grade systems, automate deployments, and ensure resilience of data pipelines, APIs, and AI services across AWS, Azure, and GCP. This is a hands-on role where cloud architecture meets engineering excellence.

Key Responsibilities

Cloud Architecture & Infrastructure

·         Design and implement cloud-native solutions on AWS, Azure, or GCP.

·         Build secure, highly available, and cost-optimized cloud infrastructure.

·         Implement networking, IAM, security, and compliance best practices.

Automation & DevOps

·         Develop Infrastructure as Code (IaC) using Terraform/CloudFormation.

·         Implement CI/CD pipelines to automate deployments for AI/ML and data platforms.

·         Enable monitoring, logging, and alerting using cloud-native or third-party tools.

Containerization & Orchestration

·         Manage Kubernetes clusters and containerized workloads (Docker, EKS/AKS/GKE).

·         Optimize workloads for scalability, performance, and cost efficiency.

Collaboration & Support

·         Partner with Data & AI teams to ensure cloud infra supports ML/LLM workloads (e.g., GPU provisioning, vector DB hosting).

·         Troubleshoot complex production issues and optimize cloud operations.

·         Mentor junior engineers on cloud best practices.

Required Skills & Qualifications

Education:Bachelor’s/Master’s in Computer Science, Cloud Computing, or related fields.

Experience:6–10 years in cloud engineering/DevOps with expertise in:

·         Cloud Platforms: AWS, Azure, or GCP (multi-cloud experience preferred).

·         Infrastructure as Code: Terraform, CloudFormation, Pulumi.

·         Containers & Orchestration: Docker, Kubernetes, Helm

·         CI/CD Tools: Jenkins, GitHub Actions, GitLab CI, or Azure DevOps

·         Networking & Security: VPCs, IAM, firewalls, VPN, secrets management.

·         Observability: Prometheus, Grafana, ELK, or cloud-native monitoring tools.

·         AI/ML Enablement (preferred): GPU provisioning, supporting MLOps pipelines.

Skills Matrix

Candidates must submit a detailed resume and fill out the following matrix:

Instructions for Candidates:

·         Provide a detailed resume highlighting cloud projects (infrastructure automation, containerization, multi-cloud deployments, AI/ML workload support).

·         Fill out the above skills matrix with accurate dates, duration, and self-ratings.