AI ML Ops

Job Purpose The AI MLOps is responsible for operating, securing, and optimizing TCB's AI platform ecosystem including AWS Bedrock, agentic AI solutions, and internal machine learning services. The role ensures AI use cases are deployed from experimentation to production in compliance with ITSM processes, security standards, and regulatory requirements of the insurance industry.
The position drives operational excellence, automation, reliability, observability, and cost optimization across the AI platform lifecycle.
Key Accountabilities (1)
AI Platform Operations & Reliability
Deploy, operate, and maintain AI infrastructure on AWS platforms including Bedrock, SageMaker, EKS, and Lambda.
Ensure platform availability, scalability, and performance in accordance with agreed SLA and latency targets.
Manage AI services lifecycle, capacity planning, and production readiness.
Support deployment and operationalization of AI use cases across business functions.
MLOps / LLMOps Pipeline Management
Design, implement, and optimize CI/CD pipelines for AI models, prompts, and AI agents.
Establish model lifecycle management including versioning, evaluation, testing, deployment, and rollback mechanisms.
Automate AI delivery processes to improve deployment reliability and reduce operational risks.
Implement best practices for model governance and production AI operations.
Key Accountabilities (2)
Monitoring, Incident & Change Management
Establish observability frameworks covering logs, metrics, traces, token consumption, and AI output quality.
Monitor system health and proactively identify operational risks.
Investigate and resolve incidents following Jira ITSM and organizational service management processes.
Support change management processes and ensure controlled deployment of AI services.
Cost Management (FinOps)
Monitor and analyze AI platform consumption and token usage across departments.
Implement budgeting controls, quota management, cost alerts, and optimization initiatives.
Produce regular utilization and cost reports for management stakeholders.
Recommend efficiency improvements to maximize AI platform ROI.
Key Accountabilities (3)
Security, Compliance & Governance
Manage access controls and authentication through Azure AD (Microsoft Entra ID) and enterprise identity services.
Implement AI guardrails and governance controls to ensure secure and responsible AI usage.
Maintain audit trails, security logs, and compliance reporting.
Ensure customer data protection and compliance with internal policies and applicable personal data protection regulations.
Collaboration & Documentation
Collaborate with software engineering, infrastructure, security, and business teams to transition AI solutions into production.
Develop and maintain operational runbooks, knowledge articles, and technical documentation on Confluence.
Provide operational guidance and best practices for AI platform users and stakeholders.
Support continuous improvement initiatives across AI operations and platform management.
Key Relationships - Direct Manager
Head of IT Operation & Infrastructure
Key Relationships - Direct Reports
Key Relationships - Internal Stakeholders
Departments in company
Key Relationships - External Stakeholders
Technology provider/vendor, security partner, professional communities,...
Success Profile - Qualification and Experiences
Qualifications
Bachelor's degree in Information Technology, Computer Science, Computer Engineering, Artificial Intelligence, or related disciplines.
Work Experience
Mandatory
Minimum 4 years of experience in DevOps, SRE, Cloud Engineering, or related infrastructure roles.
Minimum 1 year of hands-on experience operating AI/ML or Generative AI/LLM systems in production environments.
Strong experience with AWS services including IAM, VPC, Bedrock, SageMaker, EKS, Lambda, or equivalent cloud AI services.
Practical experience with Docker, Kubernetes, Terraform, and CI/CD tools such as GitHub Actions or GitLab CI.
Proficiency in Python and Bash scripting.
Good understanding of APIs, RAG (Retrieval-Augmented Generation), Vector Databases, Prompt Engineering, and Model Evaluation.
Experience with monitoring and observability platforms such as CloudWatch, Prometheus, Grafana, or similar solutions.
Knowledge of ITIL / ITSM processes and operational governance.
Preferred
AWS certifications such as:
AWS Certified Solutions Architect
AWS Certified DevOps Engineer Professional
AWS Certified Machine Learning Specialty
Experience in FinOps practices and cloud cost optimization.
Experience with Azure AD / Microsoft Entra ID and SSO integration.
Experience working in insurance, banking, or financial services environments.
Strong automation mindset and continuous improvement approach.
Foreign Language
English proficiency sufficient for reading technical documentation, collaborating with technology vendors, and supporting platform operations.