Job Summary
- 6+ years in software/AI engineering with hands-on DevOps/platform experience
- Build and deploy production AI multi-layered agents using Python
Key Responsibilities
· 6+ years in software/AI engineering with hands-on DevOps/platform experience
· Build and deploy production AI multi-layered agents using Python
· Python Expertise
Skill Requirements
· 6+ years in software/AI engineering with hands-on DevOps/platform experience
· Build and deploy production AI multi-layered agents using Python
· Python Expertise
· Strong proficiency in Python (6+ years) with a focus on building enterprise-grade backend APIs or data services.
· Experience with modern frameworks like FastAPI, Flask, or Django optimized for asynchronous execution.
· Understanding of data handling libraries (e.g., Pandas, SQLAlchemy, Pydantic) and integrating with GenAI/LLM frameworks (like LangChain, LlamaIndex, or native AI APIs) is a massive plus.
· DevOps & CI/CD
· Proven experience building and maintaining secure CI/CD pipelines using tools like GitHub Actions, GitLab CI, or Jenkins.
· Extensive experience with Infrastructure as Code (IaC) using Terraform or Ansible to manage multi-source cloud environments.
· Deep knowledge of cloud platforms (AWS, Azure, or GCP), specifically networking, security policies, and IAM management.
· Container Management & Orchestration
· Production-level expertise with Docker for containerising microservices and AI workloads.
· Deep hand-on experience managing and scaling Kubernetes (EKS, AKS, GKE, or OpenShift) clusters.
· Familiarity with Kubernetes constructs (Helm charts, ingress controllers, network policies, and cluster autoscaling).
· Data & Monitoring
· Comfortable interfacing with distributed data storage solutions (SQL, NoSQL, and Vector databases).
· Experience implementing comprehensive observability stacks (Prometheus, Grafana, ELK/PLG, Datadog) to monitor system and query performance