Engineer - ML Ops

Reltio, Trusted Data Foundation for Your Business Outcomes

About Reltio

At Reltio, we’re on a mission to enable digital transformation by delivering a single source of truth for enterprise data designed for the digital experience economy. We are disrupting the master data management (MDM) software market when we launched the first cloud-native MDM software-as-a-service (SaaS) platform. The Reltio Connected Data Platform leverages a cloud-native multi-tenant architecture and our ecosystem to enable speed, agility and flexibility at scale. Companies across industries rely on Reltio to deliver mission-critical, secure, trusted real-time data at scale to create connected omnichannel experiences for their customers, partners and employees.

We’ve earned numerous awards and top rankings for our technology, our culture and our people. Reltio was founded on a distributed workforce and offers flexible work arrangements to help our people manage their personal and professional lives. So if you’re ready to work on unrivalled technology where your desire to be part of a collaborative team is met with a laser-focused mission to enable digital transformation with connected data, let’s talk.

Reltio’s Focus:

At Reltio, you can take part in our mission to enable digital transformation by delivering a single source of truth for enterprise data designed for the digital experience economy. We contribute to more fulfilling and happier lives for people everywhere by delivering single source of truth for data enabling digital transformation. We leverage a cloud-native multi-tenant architecture and our ecosystem to enable speed, agility and flexibility at scale. We pride ourselves in our transparent, leadership, and meritocratic company culture and have achieved the Fortune 100 best companies to work for.

Reltio’s values: 

• Customers and Partners are people not companies 
• Better Together 
• Simplify and Share 
• Own it 
• Bring your all 

Leadership Expectations: 

• Be one Reltio 
• Be Candid & Caring 
• Make Data-Driven Decisions 
• Learn Teach Learn 
• Role Model All Day, Every Day 

Responsibilities

• Design, build and maintain scalable and robust infrastructure for AI/ML systems, including cloud-based environments, containerization and orchestration platforms.
• Develop and implement CI/CD pipelines to automate the deployment, testing and monitoring of AI/ML models and applications.
• Collaborate with data scientists, data engineers, software engineers and quality assurance engineers to optimize model training, release validation, deployment and inference pipelines.
• Monitor and troubleshoot AI/ML systems to ensure high availability, performance and reliability.
• Maintain and monitor model training and inference pipelines across multi-cloud tenants especially around Large Language Models (LLMs).
• Maintain Kubernetes pods, container registry and virtual machine image library and model registry
• Monitor infrastructure utilization and costs pertaining to model training, inference and GPU utilization
• Implement best practices for security, data privacy and compliance in AI/ML workflows and infrastructure.
• Evaluate and integrate new tools, technologies and frameworks to improve the efficiency and effectiveness of our MLOps processes.
• Mentor and provide technical guidance to junior members of the organization.
• Stay up-to-date with the latest advancements and trends in MLOps, DevOps and cloud technologies and share them with the team.

Requirements

• Proven 3+ years of experience in Devops with 1+ years of experience in MLOps
• Strong hands on knowledge of cloud platforms such as AWS, Azure or Google Cloud
• Proficiency in containerization technologies such as Docker and container orchestration platforms like Kubernetes.
• Experience with code versioning (Git), CI/CD tools (Jenkins) and Blue/Green deployments.
• Familiarity with MLOps tools like MLFlow and Orchestration Platforms like Databricks, Airflow
• Solid programming skills in Python and experience in scripting and automation.
• Familiarity with machine learning frameworks and libraries such as PyTorch, Tensorflow and scikit-learn.
• Deep understanding of DevOps principles, agile methodologies and software development lifecycle.
• Strong problem-solving and troubleshooting skills, with the ability to analyze and resolve complex technical issues.
• Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.
• Bachelor's or Master's degree in Computer Science, Information Systems, Data Science or a related field.

Preference

• Strong experience with ML stack oriented Cloud Services - E.g. Sagemaker, AI/ML stack, S3, Lambda, AWS Service APIs
• Past experience in monitoring and building automation for monitoring drifts in Data Quality, Model Quality, Bias Drift for Models and Feature Attribution Drift for Models