Salary
≈ $20k – $84k per year (Estimated)
Location
In office (Mumbai)
Overview
Company
Impact
Profile match
Falkonry's operational machine learning system empowers operations team to improve throughput, uptime, yield, and quality using pattern discovery, condition recognition & predictive analytics preventing failures.
As a Systems Software Engineer, you will be responsible for the infrastructure, deployment, and operational reliability of Falkonry's Time Series AI platform. You will work across cloud and edge environments, ensuring the platform runs efficiently and securely and remains compliant and supportable whether deployed on public cloud, private cloud, on-premises, or in air-gapped networks. Prior experience is welcome but not required what matters is your ability to think clearly and build reliably.
Responsibilities:
- Design, deploy, and maintain infrastructure for the TSI platform across cloud, on-premises, and edge environments.
- Build and manage CI/CD pipelines, automated testing, and release processes.
- Configure and operate container orchestration systems (Kubernetes, Docker) across diverse deployment targets.
- Monitor system health, performance, and resource utilization; build observability tooling and alerting.
- Manage data connectivity and ingestion infrastructure supporting industrial protocols.
- Harden systems for security and compliance, particularly for defense and regulated industry deployments.
- Automate infrastructure provisioning and configuration management.
- Troubleshoot production issues, perform root cause analysis, and drive reliability improvements.
- Collaborate with backend and platform engineers to optimize system architecture for performance and cost.
Requirements:
- Strong fundamentals in Linux systems administration and networking.
- Experience with or strong interest in cloud platforms (AWS, Azure, or GCP).
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Understanding of CI/CD principles and tooling (GitHub Actions, Jenkins, or similar).
- Comfort with scripting and automation (Bash, Python, or similar).
- Ability to learn quickly, work independently, and take ownership of production systems.
Nice-to-haves:
- Experience with infrastructure-as-code tools (Terraform, Ansible, Pulumi).
- Familiarity with monitoring and observability stacks (Prometheus, Grafana, ELK, or similar).
- Exposure to air-gapped, edge, or sovereign cloud deployments.
- Understanding of network security, TLS, and compliance frameworks.
- Experience supporting high-throughput data systems or streaming infrastructure.
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