Platform Engineer

Platform Engineer (AI/ML Platform) 4 to 7 Years Experience Role Summary: The Platform Engineer will be responsible for designing, building, and optimizing AI/ML platform capabilities to support scalable, secure, and reliable model deployment and operations. The role focuses on enabling seamless model lifecycle management, platform automation, and governance across enterprise AI ecosystems. Key Responsibilities: - Design, develop, and maintain AI/ML platform infrastructure for model deployment and lifecycle management - Build and manage model registry systems for versioning, lineage tracking, and governance - Implement and standardize model documentation practices (model cards, usage guidelines, risk metrics) - Develop and manage AI gateways for intelligent routing, validation, and orchestration of AI services - Ensure platform scalability, reliability, and performance through monitoring and continuous improvements - Implement responsible AI practices, including safety guardrails, validation checks, and policy enforcement - Build and manage CI/CD pipelines for ML workflows and model deployments - Collaborate with data scientists, engineers, and business teams to align platform capabilities with business requirements - Develop technical documentation, best practices, and onboarding materials Required Skills & Qualifications: - 4 7 years of experience in platform engineering, ML engineering, or software engineering roles - Strong programming skills in Python (preferred) or other relevant languages - Hands-on experience with cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) - Experience with model lifecycle management tools (MLflow, Kubeflow, or similar) - Familiarity with CI/CD pipelines and DevOps practices for ML systems - Experience with containerization and orchestration (Docker, Kubernetes) - Understanding of APIs, gateways, and microservices architecture - Knowledge of AI/ML governance, model monitoring, and validation practices Preferred Skills: - Experience with AI gateway tools (Kong, NGINX, or custom routing frameworks) - Familiarity with model validation, drift detection, and monitoring tools - Exposure to enterprise-scale AI platform design and multi-tenant systems - Understanding of data security, compliance, and responsible AI frameworks - Strong communication and stakeholder management skills Certifications (Good to Have): - Azure AI Engineer Associate / AWS ML Specialty / GCP ML Engineer - Kubernetes certifications (CKA or equivalent) - Certifications in AI governance or responsible AI practices Platform Engineer (AI/ML Platform) 4 to 7 Years Experience Role Summary: The Platform Engineer will be responsible for designing, building, and optimizing AI/ML platform capabilities to support scalable, secure, and reliable model deployment and operations. The role focuses on enabling seamless model lifecycle management, platform automation, and governance across enterprise AI ecosystems. Key Responsibilities: - Design, develop, and maintain AI/ML platform infrastructure for model deployment and lifecycle management - Build and manage model registry systems for versioning, lineage tracking, and governance - Implement and standardize model documentation practices (model cards, usage guidelines, risk metrics) - Develop and manage AI gateways for intelligent routing, validation, and orchestration of AI services - Ensure platform scalability, reliability, and performance through monitoring and continuous improvements - Implement responsible AI practices, including safety guardrails, validation checks, and policy enforcement - Build and manage CI/CD pipelines for ML workflows and model deployments - Collaborate with data scientists, engineers, and business teams to align platform capabilities with business requirements - Develop technical documentation, best practices, and onboarding materials Required Skills & Qualifications: - 4 7 years of experience in platform engineering, ML engineering, or software engineering roles - Strong programming skills in Python (preferred) or other relevant languages - Hands-on experience with cloud ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI) - Experience with model lifecycle management tools (MLflow, Kubeflow, or similar) - Familiarity with CI/CD pipelines and DevOps practices for ML systems - Experience with containerization and orchestration (Docker, Kubernetes) - Understanding of APIs, gateways, and microservices architecture - Knowledge of AI/ML governance, model monitoring, and validation practices Preferred Skills: - Experience with AI gateway tools (Kong, NGINX, or custom routing frameworks) - Familiarity with model validation, drift detection, and monitoring tools - Exposure to enterprise-scale AI platform design and multi-tenant systems - Understanding of data security, compliance, and responsible AI frameworks - Strong communication and stakeholder management skills Certifications (Good to Have): - Azure AI Engineer Associate / AWS ML Specialty / GCP ML Engineer - Kubernetes certifications (CKA or equivalent) - Certifications in AI governance or responsible AI practices Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.