Sr. Platform Engineer

Requirements:

• AWS, GCP (and/or - strict requirements)
• CI/CD
• Terraform, CloudFormation
• Python
• Docker/Kubernetes

GENERAL RESPONSIBILITIES:

The
AI/ML Sr. Platform Engineer

is responsible for designing, building, and maintaining robust, scalable, and automated cloud-based platforms that enable AI and machine learning capabilities within the organization. The role ensures that the platform supports seamless integration of AI models, DevOps pipelines, and security automation to deliver high-performing, cost-efficient, and future-ready digital solutions.

The engineer will collaborate closely with architects, developers, and data scientists to operationalize AI workloads, automate deployments, and maintain the overall health and scalability of the enterprise platform infrastructure.

DUTIES AND RESPONSIBILITIES:

• Design, implement, and manage cloud-native platforms (AWS, GCP) that support AI, ML, and DevOps workloads.
• Develop CI/CD and MLOps pipelines for automated deployment, versioning, and monitoring of AI/ML models.
• Implement infrastructure-as-code (Terraform, CloudFormation) to automate provisioning and environment management.
• Build and maintain containerized applications and orchestration systems (Docker, Kubernetes).
• Develop serverless and event-driven architectures for cost optimization and scalability of AI functions.
• Collaborate with AI/ML engineers to deploy and optimize LLMs and AI frameworks such as LangChain and Open WebUI.
• Integrate AI-driven automation into DevSecOps workflows to enhance system security, monitoring, and maintenance.
• Ensure platform reliability, availability, and compliance with security best practices and governance standards.
• Stay up to date with emerging technologies in AI systems, cloud automation, and agentic platforms to enhance platform capabilities.

FUNCTIONAL/TECHNICAL COMPETENCIES:

Information Security, Release and Deployment, Configuration Management, Methods and Tools, Systems Development Management, Systems and Software Life Cycle Engineering

CORE COMPETENCIES:

Teamwork & Collaboration, Accountability, Customer Focus, Communication, Innovation, Quality

JOB SPECIFICATIONS:

• Education

–    Bachelor's Degree in IT, Computer Science, Engineering, or a related field.
- Certifications (Preferred):
- AWS Certified DevOps Engineer – Professional
- AWS Certified Machine Learning – Specialty / Google Cloud Machine Learning Engineer
- Kubernetes Administrator (CKA) or Architect certification
- Related Work Experience

-years of experience in software engineering, DevOps, or cloud architecture.
- years of specialized experience in AI/ML platform engineering, MLOps, or related domain.
- Proven experience designing, deploying, and maintaining enterprise-grade cloud systems.
- Demonstrated expertise in scripting (Python, JavaScript, or equivalent).
- Knowledge

–   Strong understanding of the following:
- Cloud Platforms (AWS, GCP, Azure) – compute, storage, IAM, and AI services.
- DevOps and MLOps Practices (CI/CD, model registry, pipeline automation).
- AI/ML Frameworks (LangChain, TensorFlow, PyTorch, Hugging Face).
- Infrastructure-as-Code and Configuration Management (Terraform, CloudFormation).
- Containerization (Docker) and Orchestration (Kubernetes).
- Serverless Architecture, Event-driven Design, and Agentic Systems.
- Cloud Security, Governance, and Compliance Standards.
- Skills

–
- Strong analytical and problem-solving ability.
- Excellent technical documentation and presentation skills.
- Effective communication and cross-team collaboration.
- Adaptability and continuous learning mindset.
- Strong ownership, attention to detail, and innovation-driven thinking.