The MLOps Architect is responsible for architecting and scaling enterprise‑wide MLOps and LLMOps platforms across Azure, AWS, and GCP. The role emphasizes platform design, standardization, governance, and cross‑team enablement, while remaining technically hands‑on.
Roles & Responsibilities:
- Define reference architectures and standards for multi‑cloud MLOps platforms
- Design scalable ML and GenAI platforms supporting multiple teams and use cases
- Establish model lifecycle management, CI/CD, monitoring, and governance frameworks
- Architect LLMOps and RAG solutions for enterprise GenAI adoption
- Drive security, compliance, and Responsible AI controls across ML platforms
- Optimize platform‑level performance, reliability, and cost efficiency
- Mentor MLOps engineers and support capability development
- Lead technical design reviews, estimations, and solution governance
- Translate business requirements into scalable platform capabilities
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent
- 10–15 years of experience in MLOps, ML Platform Engineering, Cloud Architecture, or DevOps
- Proven experience designing enterprise‑scale ML platforms
Technical Skills:
- Strong Python and ML engineering background
- MLOps and LLMOps architecture and lifecycle management
- Kubernetes platform design and operations
- CI/CD strategy for ML platforms
- Terraform‑based Infrastructure as Code
- Deep expertise in one cloud (Azure/AWS/GCP) with working knowledge of others
- Observability, governance, and reliability engineering
- Azure / AWS / GCP Machine Learning certifications
Certifications (Preferred)
- HashiCorp Certified: Terraform Associate
- Kubernetes certifications (CKA, CKAD, or CKS).
Soft Skills
- Strong communication and stakeholder‑management skills.
- Ability to work in client‑facing or cross‑functional environments.
- Mentoring mindset with a focus on knowledge sharing and capability building.
The MLOps Architect is responsible for architecting and scaling enterprise‑wide MLOps and LLMOps platforms across Azure, AWS, and GCP. The role emphasizes platform design, standardization, governance, and cross‑team enablement, while remaining technically hands‑on.
Roles & Responsibilities:
- Define reference architectures and standards for multi‑cloud MLOps platforms
- Design scalable ML and GenAI platforms supporting multiple teams and use cases
- Establish model lifecycle management, CI/CD, monitoring, and governance frameworks
- Architect LLMOps and RAG solutions for enterprise GenAI adoption
- Drive security, compliance, and Responsible AI controls across ML platforms
- Optimize platform‑level performance, reliability, and cost efficiency
- Mentor MLOps engineers and support capability development
- Lead technical design reviews, estimations, and solution governance
- Translate business requirements into scalable platform capabilities
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent
- 10–15 years of experience in MLOps, ML Platform Engineering, Cloud Architecture, or DevOps
- Proven experience designing enterprise‑scale ML platforms
Technical Skills:
- Strong Python and ML engineering background
- MLOps and LLMOps architecture and lifecycle management
- Kubernetes platform design and operations
- CI/CD strategy for ML platforms
- Terraform‑based Infrastructure as Code
- Deep expertise in one cloud (Azure/AWS/GCP) with working knowledge of others
- Observability, governance, and reliability engineering
- Azure / AWS / GCP Machine Learning certifications
Certifications (Preferred)
- HashiCorp Certified: Terraform Associate
- Kubernetes certifications (CKA, CKAD, or CKS).
Soft Skills
- Strong communication and stakeholder‑management skills.
- Ability to work in client‑facing or cross‑functional environments.
- Mentoring mindset with a focus on knowledge sharing and capability building.
•A bachelor’s or master’s degree or equivalent •Broader experience with AWS, Azure and GCP beyond core usage•Familiarity with Waterfall delivery methodology•Ability to formulate Cloud & DevOps strategy and assess maturity across varied client environments
