Senior MLOps Engineer
📍 Location: United States | Mexico (Remote)
🏢 Industry: Information Services
💼 Work Setting: Remote
Are you passionate about building scalable AI and machine learning solutions that transform complex data into intelligent, business-driven outcomes?
Do you thrive at the intersection of machine learning, cloud engineering, and automation, enabling cutting-edge AI applications to operate reliably, securely, and at scale? We are seeking a Senior MLOps Engineer to help bridge the gap between Data Science and Engineering by developing robust platforms, pipelines, and infrastructure that support the full machine learning lifecycle.
About the Role
In this role, you will be responsible for deploying, operationalizing, and optimizing machine learning, search, recommendation, and Generative AI solutions in production environments. You will collaborate with cross-functional teams to build scalable AI platforms, automate ML workflows, improve model performance, and ensure governance, reliability, and efficiency across the MLOps ecosystem.
Key Responsibilities
ML Engineering, MLOps, and AI Platform Development
- Design, automate, and orchestrate end-to-end machine learning workflows across cloud-based and enterprise AI platforms.
- Develop and maintain model registries, artifact repositories, and governance processes to support reproducibility and compliance requirements.
- Build and manage CI/CD pipelines for machine learning applications, including automated data validation, model testing, deployment, and monitoring.
- Implement and optimize MLOps solutions using industry-standard machine learning platforms and frameworks.
- Design, develop, and maintain scalable ML pipelines supporting predictive analytics, recommendation engines, and AI-powered applications.
- Engineer retrieval and knowledge-based AI systems, including semantic search, embeddings, vector search, hybrid retrieval, and large language model integrations.
- Develop solutions leveraging search, vector database, and graph database technologies to support intelligent data retrieval and discovery.
- Create evaluation frameworks and testing methodologies for machine learning models, search relevance, and Generative AI applications.
- Monitor system performance and optimize infrastructure utilization, scalability, reliability, and operational costs.
- Stay current with emerging advancements in machine learning, natural language processing, Generative AI, and MLOps best practices, applying innovative approaches to business challenges.
Collaboration and Stakeholder Engagement
- Partner with Product Managers, Data Scientists, Domain Experts, and Responsible AI teams to translate business requirements into scalable AI solutions.
- Collaborate with Engineering and Operations teams to deploy, maintain, and support production-grade machine learning systems.
- Contribute to architectural discussions and provide technical guidance on AI platform design, deployment strategies, and operational excellence initiatives.
- Support cross-functional projects that drive innovation, improve operational efficiency, and enhance AI capabilities across the organization.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology, or a related field.
- 3-5+ years of experience in Machine Learning Engineering, MLOps, AI Platform Engineering, or related technical roles.
- Strong programming expertise in Python and experience with object-oriented programming languages such as Java or Scala.
- Hands-on experience deploying and managing machine learning solutions in production environments.
- Solid understanding of machine learning concepts, statistical analysis, model development, and natural language processing techniques.
- Experience working with major cloud platforms such as AWS, Azure, or Google Cloud.
- Knowledge of search technologies, vector databases, graph databases, and information retrieval systems.
- Experience evaluating, testing, and optimizing Generative AI and Large Language Model solutions.
- Strong understanding of the machine learning lifecycle, including feature engineering, model training, validation, deployment, and monitoring.
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar technologies.
- Familiarity with large-scale distributed data processing technologies and data engineering concepts.
- Strong analytical, troubleshooting, communication, and stakeholder management skills.
Preferred Qualifications
- Experience designing and operationalizing Generative AI, Retrieval-Augmented Generation (RAG), recommendation systems, or intelligent search solutions.
- Expertise with ML lifecycle tools such as MLflow, SageMaker, Azure Machine Learning, or equivalent platforms.
- Experience with containerization, orchestration, and infrastructure automation technologies.
- Knowledge of model governance, Responsible AI practices, and AI security controls.
- Experience implementing monitoring, observability, and performance optimization strategies for AI systems.
- Certifications in Cloud, Machine Learning, Data Engineering, or related technologies.
- Experience leading technical initiatives and mentoring engineering teams.