Bachelor’s, Master’s in computer science, machine learning, applied statistics, mathematics, engineering, artificial intelligence, or a related field
4+ years of hands-on experience as a Software or Platform Engineer, with exposure to enterprise platform deployments
Strong engineering fundamentals with proficiency in Python and familiarity with modern full-stack development (e.g., React, NextJS or equivalent)
Experience designing, deploying, and managing cloud-based systems (AWS, Azure, or GCP), including containerization (Docker) and orchestration frameworks, with hands-on experience operating and troubleshooting production systems
Deep expertise in Kubernetes cluster architecture, installation, configuration, and lifecycle management at production scale
Experience with CI/CD pipelines and Infrastructure as Code (e.g., GitHub Actions, GitLab CI, Terraform, Ansible, Helm)
Strong understanding of data architectures and platform design, including relational and graph databases (e.g., PostgreSQL, Neo4j), data pipelines, and system integration patterns across structured and unstructured data
Familiarity with AI platform concepts, including model integration patterns, agentic workflows, and how AI-driven applications interact with underlying data and infrastructure is a plus
Strong problem-solving skills with a structured approach to debugging and resolving issues in complex, non-standard environments
Experience with DevSecOps and infrastructure security practices (e.g., IAM/SSO, RBAC, secrets management, encryption) is a plus
Experience with deployment standards, runbook development, and automation frameworks is a strong advantage
Familiarity with observability, monitoring, and compliance tooling (e.g., Prometheus, Grafana, ELK) and experience working in secure or regulated environments is a plus.
Willingness to travel
Ability to communicate effectively with technical and non-technical stakeholders in client-facing settings, including delivering walkthroughs, documentation, and training