- Company Name
- Avensys Consulting UK
- Job Title
- DevOps Engineer (AI)
- Job Description
-
**Job Title**
DevOps Engineer (AI)
**Role Summary**
Lead end‑to‑end deployment of the Agentic AI platform in enterprise settings. Architect scalable, secure, and compliant solutions while integrating ML models, APIs, and data pipelines into production infrastructure. Act as senior technical lead, mentor peers, and collaborate closely with customer engineering teams.
**Expectations**
- Execute 03‑06 month contracts with potential extension.
- Fluent in French; English proficiency acceptable for documentation and client interaction.
- Deliver reliable, high‑performance AI deployments meeting enterprise security and compliance requirements.
**Key Responsibilities**
1. **Deployment Engineering**
- Design and implement end‑to‑end pipelines for Agentic platform integration.
- Deploy ML models, ensuring scalability, reliability, and monitoring.
- Automate releases via CI/CD, IaC (Terraform, Helm, Ansible), Docker & Kubernetes.
2. **AI Platform Integration & Optimization**
- Build SDKs, APIs, micro‑services for custom use cases.
- Develop data preprocessing, feature engineering, real‑time inference tooling.
- Optimize model serving, caching, and resource allocation for low latency.
3. **Reliability, Security & Compliance**
- Architect resilient, observable, and scalable systems.
- Apply enterprise security best practices (encryption, identity, network).
- Address SOC2, HIPAA, GDPR, and other regulatory constraints.
4. **Engineering Leadership & Technical Escalation**
- Lead technical resolution of complex deployment issues.
- Partner with customer engineering teams to embed platform into workflows.
- Provide field feedback to product and core engineering.
5. **Enablement & Knowledge Sharing**
- Create reusable deployment templates, automation scripts, and playbooks.
- Mentor Forward Deploy Engineers on advanced patterns and ML systems engineering.
**Required Skills**
- Strong programming in Python, TypeScript/JavaScript, or comparable backend language.
- Cloud experience (AWS, GCP, Azure) with Kubernetes, serverless frameworks.
- API design, distributed systems, and data engineering proficiency.
- Operationalizing ML models (TensorFlow, PyTorch, Hugging Face, custom inference engines).
- CI/CD, IaC (Terraform, Helm, Ansible) expertise.
- Monitoring & observability stack: Prometheus, Grafana, ELK, Datadog.
- Performance profiling, scaling strategies, SRE principles.
- Security (SSO, RBAC, encryption) and compliance awareness (SOC2, HIPAA, GDPR).
- Excellent debugging, problem‑solving, and communication skills.
- Adaptability to fast‑moving, ambiguous environments.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- Certifications in cloud platforms (AWS Certified Solutions Architect, GCP Professional Cloud Architect, or Azure Solutions Architect) and DevOps practices (e.g., Kubernetes Certified Administrator) preferred.