Designation: Technical Architect – Cloud
Skills: AWS, Azure, GCP, Python, Node.js, REST, GraphQL, MQTT
Qualification: B.E / B.Tech / M.Tech – Computer Science, Electronics, or equivalent
Location: Pune
Experience: 10 – 15 Years
Role overview
We are looking for a seasoned Technical Architect – Cloud to lead the design and delivery of scalable, secure, and intelligent cloud platforms for IoT and industrial applications. In this role, you will own cloud architecture decisions across customer engagements, spanning IoT device management, data ingestion pipelines, microservices platforms, and AI/ML workload hosting, while working closely with embedded, firmware, and application engineering teams.
Architecture & Design
- Architect scalable, secure, and high-availability cloud platforms on AWS (primary) with familiarity with Azure and GCP for multi-cloud scenarios.
- Design end-to-end IoT data architectures – from device connectivity (MQTT, AMQP, CoAP) through ingestion pipelines to analytics and dashboarding layers.
- Define reusable architecture patterns, reference frameworks, and technical standards across cloud-based product and platform engagements.
- Lead cloud-to-edge architecture decisions, ensuring seamless data flow between edge gateways, on-premise controllers, and cloud backends.
- Evaluate and recommend cloud-native services for IoT workloads including AWS IoT Core, IoT Greengrass, Kinesis, Timestream, and managed AI/ML services.
Engineering Execution
- Oversee infrastructure-as-code (IaC) and DevOps pipelines using Terraform, Ansible, Jenkins, and AWS CodePipeline for CI/CD automation.
- Lead cloud migration strategies for on-premise or legacy industrial systems, ensuring minimal operational disruption.
- Drive containerization strategy using Docker and Kubernetes (EKS/AKS) for microservices-based deployments.
- Design and govern API layers (REST, GraphQL, WebSocket) for device management, data access, and third-party integrations.
- Apply AI/ML infrastructure design – architecting pipelines for model training, deployment (SageMaker, Azure ML), and monitoring of predictive models used in industrial and building automation contexts.
Security & Optimization
- Implement cloud security best practices – IAM, encryption at rest and in transit, secrets management, network segmentation, and compliance alignment (SOC 2, ISO 27001).
- Continuously monitor, optimize, and govern cloud cost across deployments, applying FinOps principles.
- Define SLAs, observability stacks (CloudWatch, Grafana, OpenTelemetry), and incident response frameworks.
Leadership & Collaboration
- Provide technical mentorship and architectural direction to cloud and full-stack engineering teams.
- Act as the primary technical interface with customers during presales, solutioning, and delivery governance.
- Collaborate with embedded and firmware teams to define the cloud side of chip-to-cloud product architectures.
- Participate in architecture reviews, technology evaluations, and internal engineering forums.
Technology Stack
- AWS
- Azure
- GCP
- IoT Core
- Greengrass
- Docker
- Kubernetes
- Terraform
- Ansible
- Jenkins
- Python / Node.js
- REST / GraphQL
- MQTT / AMQP
- Kafka / Kinesis
- Timestream
- SageMaker
- Lambda
- PostgreSQL / DynamoDB
- Grafana / OpenTelemetry
- React / Angular
Required Qualifications
- Cloud Expertise: 10+ years in cloud/software engineering with 5+ years in cloud architecture roles. AWS Solutions Architect – Professional (or equivalent) certification preferred.
- AWS Core Services: Hands-on expertise with EC2, S3, Lambda, RDS, DynamoDB, VPC, IAM, EKS, IoT Core, and Kinesis.
- Microservices & Containers: Deep experience with Docker, Kubernetes, and service mesh architectures (Istio/Linkerd).
- DevOps & IaC: Proficiency in Terraform, Ansible, Jenkins, GitLab CI/CD, or AWS CodePipeline.
- Programming: Strong in Python and/or Node.js/TypeScript; working knowledge of Java or Go is a plus.
- API Design: Experience building and governing RESTful and event-driven API ecosystems.
- IoT Protocols: Familiarity with MQTT, AMQP, CoAP, and data streaming architectures for industrial IoT workloads.
- Security: Solid grounding in cloud security practices, zero-trust architectures, and regulatory compliance.
Good to Have
- Exposure to industrial or building automation domains.
- Experience designing AI/ML pipelines for edge or cloud – model training, inference deployment, and MLOps practices.
- Familiarity with serverless and event-driven architectures for real-time IoT data processing.
- Knowledge of time-series databases (InfluxDB, AWS Timestream) for industrial telemetry storage.
- Cloud cost optimization (FinOps) and multi-cloud strategy experience.
- Understanding of digital twin concepts and platforms (AWS IoT TwinMaker, Azure Digital Twins).
