- Company Name
- Avensys Consulting
- Job Title
- DevOps - CI/CD Deployment Engineer
- Job Description
-
Job Title: DevOps – CI/CD Deployment Engineer
Role Summary:
Lead the end‑to‑end deployment of a cloud‑native AI platform into enterprise environments. Design, build, and maintain robust CI/CD pipelines, infrastructure‑as‑code, and container orchestration to deliver machine‑learning models at scale while ensuring reliability, security, and regulatory compliance. Act as the senior technical guide for customer deployments and provide feedback to product teams.
Expactations:
- Deliver fully automated, end‑to‑end deployment solutions for AI platforms within defined timelines.
- Own the deployment architecture, ensuring scalability, high availability, and adherence to enterprise security and compliance standards.
- Resolve complex technical challenges during customer roll‑outs and mentor junior deployment engineers.
- Provide actionable insights on performance, scalability, and integration to product and engineering leadership.
Key Responsibilities:
- Design, implement, and maintain CI/CD pipelines, IaC scripts (Terraform, Helm, Ansible), and Docker/Kubernetes clusters for ML model deployment.
- Integrate customer data sources, APIs, and systems of record into the platform, building microservices, SDKs, and extensions as needed.
- Automate data preprocessing, feature engineering, and real‑time inference pipelines.
- Optimize model serving (caching, resource allocation) for low‑latency, high‑throughput environments.
- Define and enforce security best practices (encryption, identity management, network segmentation) and compliance (SOC2, HIPAA, GDPR).
- Develop monitoring, observability, and alerting stacks using Prometheus, Grafana, ELK, or Datadog.
- Create reusable deployment templates, playbooks, and documentation to accelerate future projects.
- Mentor and train other deployment engineers on advanced DevOps techniques and ML operationalization.
Required Skills:
- 10+ years in software engineering, infrastructure engineering, or applied ML engineering.
- Proficiency in Python; familiarity with TypeScript/JavaScript or other backend languages.
- Deep knowledge of Kubernetes, container orchestration, and serverless frameworks on AWS, GCP, or Azure.
- Expertise in CI/CD, IaC (Terraform, Helm, Ansible), and scripting.
- Strong understanding of API design, distributed systems, and data engineering pipelines.
- Hands‑on experience operationalizing ML models (TensorFlow, PyTorch, Hugging Face, or custom inference engines).
- Experience with monitoring, observability, and alerting tools (Prometheus, Grafana, ELK, Datadog).
- Familiarity with compliance frameworks (SOC2, HIPAA, GDPR) and SaaS security models (SSO, RBAC, encryption).
- Excellent problem‑solving, communication, and mentorship capabilities.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- Relevant certifications such as AWS Certified DevOps Engineer, Google Professional Cloud DevOps Engineer, Kubernetes Administrator (CKA), or Red Hat Certified Engineer (RHCE) are preferred.