Experience: 6 to 8 years in DevOps / Cloud / Infrastructure
Location: Hyderabad (work from office)
About the Role
Divami builds software for clients across web, mobile and enterprise platforms, including a growing practice building solutions with AI. Our DevOps team owns the full delivery path behind that work: environments, pipelines, cloud infrastructure, security and internal IT. As DevOps Lead you will run this team day to day, set its technical direction and raise its standard toward DevSecOps and platform engineering.
Divami is a people-first company. We hold ourselves to the highest standard of work, and we believe that standard is reached by the people doing it, so their growth, wellbeing and voice matter as much as the delivery itself. We are looking for a lead who cares about both.
What You Will Do
- Own the cloud and delivery stack for Dev, QA, Staging, Pre-Prod and UAT environments across projects, and manage production environments for the projects where we host and operate them.
- Support production deployments on client-managed infrastructure by guiding client teams and sharing deployment practices.
- Host and support our AI practice: in-house AI applications used for customer demos and proofs of concept, and self-hosted open-source models on AWS used for testing and experimentation.
- Design, build and review Infrastructure as Code so every environment is reproducible, versioned and auditable.
- Build and improve CI/CD pipelines with security checks built in (SAST, SCA, secrets and container scanning).
- Lead cloud security: IAM, network design, secrets management, hardening, vulnerability management and audit readiness.
- Move the team toward a platform engineering model with reusable templates, self-service environments and clear golden paths for project teams.
- Run observability, backup, disaster recovery and cost optimisation across projects.
- Oversee internal IT: manage IT assets and coordinate with IT vendors on support, procurement and issue resolution.
- Manage AI tool subscriptions used by engineering, design and QA teams: licences, access, usage and cost, and secure usage practices.
- Provide on-call support for critical issues and planned production maintenance. This is need-based, not 24x7.
- Mentor engineers through code reviews, technical guidance and skill planning. Take part in hiring and performance reviews alongside other managers.
- Document runbooks, standards and architecture so knowledge lives with the team, not with individuals.
What You Bring
Must have
- 6 to 8 years of hands-on DevOps / cloud engineering, including time as a lead or technical owner.
- Deep AWS expertise: compute, networking (VPC), IAM, storage, containers (ECS/EKS), databases, monitoring and cost control.
- Strong Infrastructure as Code with Terraform or an equivalent, plus configuration management.
- Production CI/CD experience with tools such as GitHub Actions, GitLab CI or Jenkins.
- Containers and orchestration (Docker, Kubernetes or ECS) in real delivery environments.
- Practical security knowledge: IAM hygiene, secrets management, vulnerability scanning, secure pipelines, hardening.
- Observability experience (for example Prometheus, Grafana, CloudWatch or ELK) and structured incident handling.
- Scripting in Bash and Python or similar.
- Working knowledge of LLMOps within the scope of AI application hosting: deploying and running open-source models (for example on GPU or CPU EC2 instances), serving and API exposure, and monitoring usage, performance and cost.
- Familiarity with IT asset management and the ability to manage vendor relationships and communication.
- Hands-on technical credibility: able to build and troubleshoot directly, and to explain the reasoning behind design choices.
- Ability to mentor and grow junior engineers with patience and respect, guiding their work without taking it over, and to build a team that people want to stay in and grow with.
- Sound judgement in balancing delivery speed with security and reliability, while holding every deliverable to the highest standard of quality, with the ability to push back with clear reasons.
- Clear communication with developers, QA and project managers.
- Comfort working in a collaborative, in-office team environment.
Good to have
- Working knowledge of Azure or GCP and multi-cloud design.
- Platform engineering experience (internal developer platforms, GitOps, Backstage or similar).
- Exposure to compliance frameworks such as ISO 27001 or SOC 2.
- Exposure to AI-specific security and governance concerns such as data handling, API key management and responsible use of AI tools and models.
- Familiarity with AWS AI services (for example Bedrock or SageMaker) and model-serving tools (for example vLLM or Ollama).
- AWS certifications (Solutions Architect Professional, DevOps Professional, Security Specialty) or Kubernetes certifications.
