intellimation.ai - DevOps Engineer - CI/CD Pipeline

5 years

0 Lacs

Posted:4 days ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

Job Description

What are we looking for?

We're seeking an experienced DevOps Engineer to join our engineering team and help design, build, and scale secure, high-performance infrastructure for our Cognitive Automation product. This role is not just about managing pipelines and cloud platforms-it's about architecting resilient systems, driving automation, and enabling seamless deployments across global financial firms. We're looking for self-driven, energetic professionals eager to innovate in a fast-paced, domain-tech environment.

Responsibilities

  • Design, build, and maintain scalable CI/CD pipelines using tools like Jenkins, GitLab CI, or GitHub Actions.
  • Manage and optimize cloud infrastructure on AWS and Azure.
  • Automate infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation.
  • Implement and manage containerization with Docker and orchestration with Kubernetes.
  • Monitor system performance, reliability, and security using tools like Prometheus, Grafana, ELK, or similar.
  • Collaborate with development, QA, and product teams to streamline development-to-deployment workflows.
  • Perform root cause analysis for production errors and implement fixes and improvements.
  • Design and implement cost-effective infrastructure solutions that optimize performance, scalability, and resource utilization across cloud and hybrid environments.
  • Ensure High Availability (HA) and fault tolerance in all infrastructure components to meet business continuity and uptime requirements.
  • Build and manage secure infrastructure in line with financial industry regulations (e.g., PCI-DSS, SOC 2, ISO 27001), applying security-by-design principles across the DevOps lifecycle.
  • Proven experience in migrating infrastructure and workloads from On-Premise to Cloud and Cloud to On-Prem, with deep understanding of hybrid cloud architecture, network connectivity, and data transfer strategies.
  • Hands-on familiarity with most major cloud services and features in AWS and Azure, including but not limited to compute, storage, networking, IAM, monitoring, autoscaling, disaster recovery, and cost management tools.

Requisites

  • Bachelor's degree in computer science, Engineering, or related field (or equivalent experience).
  • 5+ years of experience in a DevOps or Site Reliability Engineering (SRE) role.
  • Hands-on experience with AWS and Azure cloud platforms (compute, storage, networking, IAM).
  • Strong knowledge of CI/CD tools (e.g., Jenkins, GitLab CI/CD, GitHub Actions, Azure DevOps).
  • Experience with Docker, Kubernetes, and container orchestration.
  • Proficiency in scripting languages such as Bash, Python, or PowerShell.
  • Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack, CloudWatch).
  • Solid understanding of networking concepts, load balancing, and DNS.
  • Experience in managing hybrid or multi-cloud environments.
  • Familiarity with SIEM tools, vaulting solutions (e.g., HashiCorp Vault), and network security.
  • Knowledge of financial data security standards: PCI-DSS, GDPR, SOX, etc.
  • Hands on experience in writing deployment pipeline files/script.

Good To Have

  • Certifications like AWS Certified DevOps Engineer, Azure DevOps Engineer Expert, etc.
  • Experience working in financial services, banking, or fintech environments.
  • Familiarity with GitOps principles and tools like ArgoCD or Flux.
  • Familiarity with AI/ML services and integrations offered by major cloud providers (e.g., AWS SageMaker, Azure ML, Vertex AI) and the ability to support infrastructure for data science and machine learning workflows.
  • Experience with Google Cloud Platform (GCP) services such as Compute Engine, Cloud Functions, BigQuery, and GKE.
  • Exposure to multi-cloud or cloud-agnostic architectures, ensuring portability and resilience across AWS, Azure, and GCP.
  • Basic understanding of MLOps practices, pipelines, and model deployment lifecycle.
(ref:hirist.tech)

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