About this role
You’ll help reduce repetitive operational work by identifying tasks that can be automated, building safe AI-assisted workflows, and improving how teams detect, diagnose, and prevent incidents. You’ll also help grow the team’s practical AI capabilities.
Education: Bachelor’s or master’s degree in computer science, Engineering, Maths or Science
Performed any modern NLP/LLM courses/open competitions is also welcomed.
Technical Requirements:
- Agentic AI workflows: Design multi-step AI-agent workflows with tool calling, state management, guardrails, human approvals, and fallback handling.
- Python scripting: Build and test maintainable Python automations using tools such as pytest and Ruff.
- Workflow automation: Automate repetitive operations using scripts, REST APIs, webhooks, and workflow orchestration tools.
- GenAI and RAG: Integrate LLMs with prompts, approved knowledge sources, retrieval-augmented generation, and output evaluation.
- Data and predictive analytics: Use SQL, pandas, and scikit-learn to analyze operational data and develop anomaly-detection or forecasting solutions.
- Observability: Use monitoring, logging, and tracing tools to support automation; f
- Security and quality: Apply least-privilege access, secure credential handling, audit logging, automated testing, and safe failure controls.
- CI/CD and deployments: Configure Git-based pipelines for automated testing, deployment, approvals, and rollback.
- Cloud fundamentals: Understand cloud services, identity and access management, networking, storage, compute, and cloud security.
- Containers and infrastructure: Deploy and troubleshoot containerized workloads using Docker; Kubernetes or Terraform experience is preferred.