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About Us:

Iron Mountain Incorporated (NYSE: IRM) provides information management services that help organizations lower the costs, risks and inefficiencies of managing their physical and digital data. The company's solutions enable customers to protect and better use their information—regardless of its format, location or lifecycle stage—so they can optimize their business and ensure proper recovery, compliance and discovery. Founded in 1951, Iron Mountain manages billions of information assets, including business records, electronic files, medical data, emails and more for organizations around the world.

Visit www.ironmountain.com for more information.

Job Summary:

At Iron Mountain, we protect what our customers value most, from the everyday to the extraordinary, while helping them bridge the physical and digital world. Today, we help our customers, including 95% of the Fortune 1000, to preserve priceless art, restore classic films, outsource their data centers around the globe and more. Our people have the opportunity to bring their creativity to a workplace that thrives on change. Here you will be part of a team that doesn't just embrace what's exceptional. It is exceptional. The Global Technology team at Iron Mountain deploys data center space to address growing customer needs and fast. Global Technology offers its customers a set of data center solutions that afforded greater convenience and accessibility. The ML Engineer role will be a part of the Global Product Engineering team at Iron Mountain.

Iron Mountain InSight is a content services platform that provides actionable business insights and predictive analytics through Machine Learning (ML)-based classification of a company’s physical and digital information, which adds structure, context and meta-data to information to make it more usable. The resulting enriched content can then enable enhanced automated governance and workflows for our customers.

The MLOps Engineer will be responsible for designing, implementing, and optimizing machine learning operations solutions within an agile development framework. The ideal candidate will have a strong background in deploying and managing machine learning models and pipelines in cloud environments, with a focus on scalability, reliability, and performance optimization.

Key Areas of Responsibility:

• Deploy machine learning models into production environments, ensuring scalability, reliability, and performance. Implement deployment pipelines and automation scripts for seamless model deployment and rollback.

• Provision and manage infrastructure resources for model training, inference, and monitoring. Utilize cloud services and containerization technologies for scalable and cost-effective infrastructure management.

• Implement CI/CD pipelines for automated testing, building, and deploying machine learning models. Enable rapid and reliable model updates and releases while maintaining version control and reproducibility.

• Monitor deployed models for performance metrics, data drift, and concept drift. Implement monitoring dashboards and alerts to detect anomalies and ensure model reliability. Optimize model performance and resource utilization based on monitoring insights.

• Manage training data and model artifacts, ensuring traceability and reproducibility of model experiments and iterations. Implement data versioning and lineage tracking to facilitate model auditing and compliance.

• Implement security controls and access policies to protect sensitive data and model assets. Ensure compliance with regulatory requirements and industry standards for data privacy and security.

• Plan and scale infrastructure resources to accommodate growing model workloads and data volumes. Perform capacity planning and performance tuning to optimize resource utilization and cost efficiency.

• Provide education and training to internal teams on MLOps best practices, tools, and methodologies. Foster a culture of collaboration and continuous learning to drive adoption of MLOps principles across the organization.

• Strong problem-solving skills and the ability to troubleshoot complex issues in machine learning workflows, infrastructure, and deployments. Capability to analyze logs, metrics, and performance data to identify and resolve issues promptly.

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Skills Required:

• Candidates with 6+ years of relevant experience in DevOps practices, cloud computing, containerization (e.g., Docker, Kubernetes), version control systems (e.g., Git), and machine learning frameworks (Kubeflow / MLFlow).

• Knowledge and hands-on experience with

• Cloud Infrastructure such as GCP, AWS, or Azure on Network, Security, IAM, and DNS related services.

• Git Platforms such as Gitlab (Preferred), Github, or similar for enabling Continuous Delivery and Release Management using provided CI tools

• Databases such as NoSQL or GraphDB

• Linux system administration or bash scripting.

• Strong hands-on experience building enterprise scalable Kubernetes infrastructure

• Ability to demonstrate knowledge of applying Observability to a network of clusters using open source stack

• Capable of debugging production outages related to resources such as network connectivity, DNS resolution, IP shortage, low disk space, crashing pods to keep the systems within strict SLAs while being cost effective

• Strong understanding with containerization technologies such as Docker. Ability to containerize machine learning applications and manage containerized deployments efficiently.

• Strong hands-on experience in building Gitlab CICD pipelines.

• Strong Python knowledge is essential to develop and publish PyPi packages in addition to writing API services.

• Exposure to ML Concepts, algorithms, and techniques/ Data Engineering / Orchestration Engines is highly desirable.

• Knowledge of monitoring and logging tools for tracking the performance, health, and reliability of machine learning models and infrastructure components. Experience with tools like Prometheus, Grafana, ELK stack (Elasticsearch, Logstash, Kibana) is beneficial.

• Experience in working with distributed teams

Qualification Required:

BE, BTech or MCA from accredited/recognized university

Disclaimer:

This job description is not an all-inclusive statement of every duty and responsibility and is not necessarily limited to the above written statements. They may be subject to review. All positions within Iron Mountain may include other duties as assigned.

Iron Mountain is an ‘Equal Opportunity Employer’ and does not discriminate on the basis of race, religion, color, creed, age, national origin, sex, sexual orientation or any physical disability .

Category: Information Technology

Iron Mountain is a global leader in storage and information management services trusted by more than 225,000 organizations in 60 countries. We safeguard billions of our customers’ assets, including critical business information, highly sensitive data, and invaluable cultural and historic artifacts. Take a look at our history here .

Iron Mountain helps lower cost and risk, comply with regulations, recover from disaster, and enable digital and sustainable solutions, whether in information management, digital transformation, secure storage and destruction, data center operations, cloud services, or art storage and logistics. Please see our Values and Code of Ethics for a look at our principles and aspirations in elevating the power of our work together.

Requisition: J0073354