Devops/MLOps - Manager

Role Value Proposition
The Manager DevOps & MLOps will provide technology leadership for building, scaling, and modernizing DevOps and MLOps capabilities across the Data & Analytics ecosystem.
The role will be responsible for defining and implementing DevOps and MLOps strategy, cloud engineering practices, CI/CD automation, ML deployment frameworks, platform engineering, containerization, and operational governance across on-premises and Azure environments.
The successful candidate will lead technical solutioning, drive engineering best practices, manage delivery and stakeholder relationships, and enable scalable deployment of Data, Analytics, AI/ML, and GenAI solutions into enterprise production environments.
This is a leadership role requiring a strong combination of technical depth, architecture/solutioning expertise, delivery leadership, stakeholder management, and team leadership.

Key Responsibilities
DevOps & MLOps Leadership

• Define and drive the DevOps and MLOps roadmap and strategy across the Data & Analytics organization.
• Lead the design, implementation, and modernization of CI/CD pipelines for Data, Analytics, AI/ML, and application workloads.
• Establish standardized DevOps and MLOps frameworks, reusable components, engineering standards, and deployment patterns.
• Drive automation across the complete ML lifecycle, including model development, validation, deployment, monitoring, retraining, and release management.
• Lead implementation of deployment pipelines for both batch and real-time ML models across on-premises and Azure environments.
• Drive adoption of Infrastructure as Code, automation, containerization, and platform engineering practices.
• Establish scalable and reusable deployment frameworks across multiple Data & Analytics teams.

Azure Cloud & Platform Engineering

• Provide technical leadership for Azure-based DevOps and MLOps platforms.

• Drive architecture and implementation across services including:

• Azure DevOps

• Azure Kubernetes Service (AKS)

• Azure Databricks

• Azure Synapse

• Azure ML

• Azure App Services

• Azure Key Vault

• Azure RBAC

• Lead hybrid cloud solutions spanning on-premises infrastructure and Azure cloud environments.

• Optimize cloud platforms for scalability, performance, security, reliability, and cost efficiency.

• Establish enterprise standards for cloud environments, deployment models, access management, and operational controls.

MLOps & AI/ML Engineering

• Lead the implementation of enterprise-grade MLOps capabilities supporting Data Science and AI/ML teams.
• Establish frameworks for model packaging, deployment, versioning, monitoring, governance, and lifecycle management.
• Enable productionization of machine learning and AI solutions through automated CI/CD and deployment pipelines.
• Provide technical leadership for MLflow, Azure ML and ML model serving/monitoring frameworks.
• Partner with Data Scientists, Data Engineers, Data Architects, and AI teams to transition models from experimentation to production.
• Drive adoption of emerging Generative AI, Agentic AI, and AI engineering practices within DevOps/MLOps platforms.

Containerization & Automation

• Lead enterprise adoption of Docker and Kubernetes/AKS for application, data, and ML workloads.
• Establish containerization and orchestration standards across the organization.
• Drive automation using Python, PowerShell, Bash, YAML, CLI and scripting frameworks.
• Develop reusable automation frameworks for infrastructure provisioning, application deployment, ML deployment, and environment management.
• Promote Infrastructure as Code and automated environment provisioning practices.

Observability & Production Engineering

• Establish enterprise-level monitoring, logging, alerting, and observability frameworks for DevOps, Data, and ML workloads.
• Define operational KPIs and engineering standards for application and ML platform performance.
• Drive proactive identification and resolution of deployment, infrastructure, and model-performance issues.
• Ensure production platforms meet agreed standards for availability, scalability, security, and performance.
• Partner with engineering and operations teams to continuously improve production readiness.

Security, Governance & Compliance

• Embed security and governance controls throughout DevOps and MLOps pipelines.

• Ensure compliance with enterprise security, risk, governance, and regulatory standards.

• Drive implementation of:

• RBAC

• Azure Key Vault

• CyberArk

• Secure secrets management

• Identity and access controls

• Secure CI/CD practices

• Establish DevSecOps practices across the development and deployment lifecycle.

• Ensure appropriate controls for ML models, data pipelines, cloud environments, and production deployments.

Technical Leadership & Solutioning

• Independently lead technical design, architecture, solutioning, estimation, and implementation planning.
• Review and approve technical designs, deployment architectures, automation frameworks, and cloud solutions.
• Identify opportunities for platform standardization, automation, modernization, and cost optimization.
• Provide technical mentorship and guidance to DevOps, MLOps, Cloud, Data Engineering, and Data Science teams.
• Drive engineering excellence and continuous improvement across the technology ecosystem.

People & Delivery Leadership

• Lead and mentor a team of DevOps/MLOps/Cloud engineers and technical professionals.
• Define technical objectives, development plans, and delivery priorities for the team.
• Manage delivery across multiple initiatives, ensuring quality, timelines, and business outcomes.
• Build a culture of automation, ownership, engineering excellence, and continuous learning.
• Support hiring, capability development, technical mentoring, and succession planning.
• Track delivery metrics, risks, dependencies, and resource requirements.

Stakeholder Management

• Collaborate extensively with stakeholders across:

• Business

• Global Technology

• Data & Analytics

• Data Engineering

• Data Science

• Data Governance

• Data Quality

• Data Architecture

• DevOps

• BI & Insights

• Operations

• Cybersecurity

• Act as a senior technology partner to global stakeholders and business leadership.

• Translate business requirements into scalable technical solutions.

• Communicate technical strategy, risks, opportunities, and delivery status to senior leadership.

Knowledge, Skills & Abilities
Education
Bachelor's degree in Computer Science, Information Technology, Engineering, or an equivalent educational qualification.
Experience

• 14+ years of overall IT experience, with significant experience in DevOps, MLOps, Cloud Engineering, Platform Engineering, or related areas.
• Strong experience leading DevOps and MLOps transformation initiatives in large enterprise environments.
• Proven experience managing technical teams and delivering complex technology programs.
• Strong experience working across on-premises and Azure cloud environments.
• Experience working with global stakeholders and distributed technology teams.

Technical Skills
DevOps & CI/CD Must Have

• Azure DevOps
• CI/CD pipeline architecture
• YAML
• Git / code versioning
• Release automation
• Deployment automation
• DevSecOps
• Infrastructure as Code
• Environment automation

MLOps Must Have

• ML model deployment and lifecycle management
• MLflow
• Azure ML
• Model serving and monitoring
• Batch and real-time ML deployment
• ML CI/CD
• Model versioning and governance
• Productionization of Data Science / AI solutions

Azure Strong Expertise

• Azure DevOps
• Azure Kubernetes Service (AKS)
• Azure Databricks
• Azure Synapse
• Azure ML
• Azure App Services
• Azure Key Vault
• Azure RBAC
• Azure CLI

Containerization & Orchestration

• Docker
• Kubernetes
• AKS
• Container lifecycle management
• Microservices deployment
• Container security

Programming & Scripting

• Python
• PowerShell
• Bash
• Shell scripting
• YAML
• CLI automation

Data & Big Data

• Apache Spark
• Hadoop
• Databricks
• SQL
• Data pipeline deployment and automation

APIs & Integration

• REST APIs
• HTTP / SSL
• JDBC
• XML
• Postman

Leadership Competencies

• Strong technical leadership and architecture capability
• Strategic thinking and technology roadmap development
• Strong stakeholder and partner management
• Excellent communication and presentation skills
• Structured problem-solving and analytical ability
• Strong delivery and execution orientation
• Ability to influence across global technology teams
• Team leadership, mentoring, and talent development
• Ability to manage multiple priorities and complex dependencies
• Strong understanding of enterprise governance, security, and compliance

Good to Have

• DevOps / Cloud certification such as Azure DevOps, Azure Solutions Architect, or equivalent
• Kubernetes certification
• Experience with Infrastructure as Code such as Terraform
• Experience with DevSecOps frameworks
• Exposure to CyberArk and enterprise access management
• Experience with RBAC and enterprise security controls
• Experience with Generative AI / Agentic AI platforms
• Experience building MLOps platforms for enterprise AI/ML teams
• Experience with FinOps / Azure cloud cost optimization
• Experience in large-scale Global Capability Center (GCC) environments

Ideal Candidate Profile
The ideal candidate is a 14+ year technology leader with strong hands-on and leadership experience across DevOps, MLOps, Azure Cloud, CI/CD, Kubernetes, Docker, ML deployment, and platform engineering.
The candidate should have demonstrated experience in leading DevOps/MLOps transformation, building scalable CI/CD platforms, enabling AI/ML productionization, managing engineering teams, and partnering with senior global stakeholders.
The role is best suited for professionals currently working as:
Manager DevOps | Senior Manager DevOps | DevOps Manager | MLOps Manager | MLOps Lead | Cloud & DevOps Manager | ML Platform Manager | DevOps/MLOps Architect | Cloud Engineering Manager | AI/ML Platform Lead
Core Focus
DevOps + MLOps + Azure Cloud + CI/CD + Kubernetes + ML Deployment + Platform Engineering + Technical Leadership