DevOps Engineer (with AI Product Development Focus)
Experience Level:
3-4 Years
Location:
Onsite
Department:
Technology, Cloud & AI Automation
Reports To:
Lead DevOps Engineer / Head of Engineering
About the Role
We are seeking a versatile and proactive DevOps Engineer skilled in managing modern cloud environments (AWS, GCP, Azure) and eager to actively contribute to the life cycle of AI-powered software product development. This position is ideal for candidates passionate about driving automation, reliability, and scalability in production systems, with the additional opportunity to directly innovate on our AI-enhanced product roadmap.
When DevOps workloads are low, you will play a significant role in building AI-driven product features or internal tools using modern AI development frameworks and platforms.
Key Responsibilities
DevOps and Infrastructure (Primary)
Cloud Infrastructure Management: Design, deploy, and manage scalable, secure, and high-availability infrastructure across AWS, GCP, and Azure.
CI/CD Pipeline Development: Build and maintain robust continuous integration and deployment pipelines (Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps).
Infrastructure as Code (IaC): Implement and manage infrastructure using Terraform, CloudFormation, or Ansible.
Containerization & Orchestration: Deploy, scale, and monitor microservices using Docker and Kubernetes (EKS, GKE, AKS).
Monitoring & Logging: Implement observability solutions (Prometheus, Grafana, ELK/OpenSearch, CloudWatch) for proactive performance tracking and alerting.
Automation & Scripting: Automate recurring tasks and workflows using Python, Bash, or PowerShell; prioritize code reusability and maintainability.
Security & Compliance: Enforce cloud/application security best practices (IAM, vulnerability assessments, compliance automation).
AI in DevOps: Experiment with and implement AI/ML-driven DevOps solutions-predictive deployment analytics, intelligent incident response, automated resource scaling.
AI-based Product Development (Secondary-when DevOps workload is light)
AI Tool Integration: Develop or enhance internal tools and product features using LLMs and AI platforms (e.g., OpenAI, AWS SageMaker, GCP Vertex AI, Azure ML).
Data Pipeline Automation: Build and automate pipelines to streamline data ingestion and model deployment.
Prototyping: Contribute to prototyping AI-powered product modules or MVPs, collaborating with software and data engineering teams.
AI Workflow Support: Support feature engineering, model evaluation, or integration of ML ops practices into the product stack.
Technical Documentation: Create technical documentation and user guides for the AI-driven functionalities and tools developed.
Required Skills and Qualifications
Bachelor's degree in Computer Science, IT, or relevant field.
3-4 years of DevOps, Cloud Infrastructure, and CI/CD implementation experience.
Proficient with at least one major cloud provider (AWS, GCP, Azure).
Familiarity with container orchestration (Kubernetes, Docker).
Experience in at least one scripting/programming language-Python preferred.
Prior engagement with AI/ML tools/platforms (e.g., OpenAI API, SageMaker, Vertex AI, or custom LLM solutions).
Familiarity with software development frameworks (Node.js, Python Flask/FastAPI, Java Spring Boot).
Excellent communication, teamwork, and problem-solving skills.
Desirable / Nice-to-Have
Knowledge of ML Ops, AI Ops, or DevSecOps practices.
Demonstrated experience building software products using AI platforms or tools.
Relevant certifications in DevOps or AI/cloud technologies.
Willingness to lead internal workshops or brown-bag sessions to further AI skills within the DevOps team.
Salary Range
?6,50,000 - ?11,00,000 per annum
Job Type: Full-time
Pay: ?650,000.00 - ?1,100,000.00 per year
Benefits:
Food provided
Paid sick time
Paid time off
Provident Fund
Work from home
Ability to commute/relocate:
Sreekaryam, Thiruvananthapuram, Kerala: Reliably commute or planning to relocate before starting work (Required)
Experience:
DevOps: 3 years (Required)
Work Location: In person
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