Job Summary We are seeking a highly skilled and motivated Machine Learning Operations (MLOps) Engineer to join our dynamic team at Speria MTech. The ideal candidate will play a crucial role in operationalizing machine learning and optimization systems by building and maintaining the infrastructure, deployment workflows, and platform capabilities required to run Applied AI solutions reliably in production. This role focuses on model deployment, scalable serving, orchestration, monitoring, and lifecycle management across Speria's integrated platforms. The MLOps Engineer works closely with Machine Learning Engineers and Data Engineers to ensure that models and decisioning systems are production-ready, observable, cost-efficient, and seamlessly integrated into downstream applications and workflows. The role also helps improve platform performance and system efficiency by standardizing deployment patterns, reducing operational complexity, and optimizing how machine learning services are exposed and consumed across the organization. We seek a solution-oriented individual who can provide answers rather than just identify problems. Embracing continuous change is key, as innovation and improvement are integral to Speria MTech's culture. This person should have a service-minded attitude, demonstrating a passion for enhancing the work of others and simplifying processes for stakeholders. Essential Functions & Responsibilities Essential responsibilities include and functions of the Machine Learning Operations Engineer are: • Build and maintain deployment pipelines for machine learning and optimization services across development, testing, and production environments. • Design and operate scalable model serving patterns, including APIs, batch jobs, and scheduled workflows that expose machine learning capabilities to downstream systems. • Manage model lifecycle workflows, including model packaging, versioning, promotion, rollback, and deployment automation. • Implement and maintain platform capabilities for observability, monitoring, and alerting across model services and related production workflows. • Optimize model-serving systems for performance, scalability, reliability, and cost efficiency in cloud environments. • Collaborate with Machine Learning Engineers to productionize models, decisioning systems, and intelligent workflows. • Work with Data Engineers to ensure production services have reliable access to required data inputs, feature outputs, and supporting data pipelines. • Standardize deployment practices, tooling, and operational patterns to reduce operational complexity and improve consistency across Applied AI systems. • Support orchestration of workflows that connect models and decisioning systems to downstream applications and operational processes. • Maintain documentation for deployment architectures, platform workflows, monitoring standards, and operational runbooks. Qualifications, Skills, and Experience • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. • 2-4 years of experience in software engineering, data engineering, MLOps, or platform engineering roles. • Experience building and maintaining production systems, including deployment pipelines or distributed systems. • Experience working with cloud-based environments for deploying and operating data or machine learning systems. • Strong programming skills in Python and experience with scripting and automation for deployment workflows. • Experience working with machine learning lifecycle tools and platforms (e.g., MLflow or similar). • Experience designing and managing CI/CD pipelines and deployment workflows for machine learning systems. • Experience with Databricks or similar platforms for machine learning lifecycle management, including model tracking, governance, and serving, is highly desirable. • Experience implementing monitoring, logging, and observability for production systems. • Strong understanding of system performance optimization, scalability, and cost efficiency. Preferred Skills • Familiarity with cloud-native data and compute services (e.g., serverless compute, managed databases, container platforms) is a plus. • Experience working with containerization technologies (e.g., Docker, container platforms, or similar). • Familiarity with deploying and managing containerized applications in cloud environments. • Experience working with CI/CD pipelines, automation, or infrastructure-as-code tools. • Experience supporting or operating machine learning systems in production environments. • Familiarity with API development and model serving patterns (REST APIs, batch inference workflows). • Ability to collaborate effectively with machine learning, data engineering, and platform teams. • Familiarity with machine learning workflows and lifecycle processes, including model deployment, monitoring, and retraining.