Mlops engineer

Job description

Overview We are looking for a hands-on Senior Machine Learning Engineer to join our team and help bridge the gap between Data Science and Engineering. This role will focus on designing, deploying, and scaling AI/ML solutions, building robust MLOps capabilities, and enabling the successful transition of machine learning models from experimentation to production. The ideal candidate will combine strong engineering skills with cloud, data, and ML deployment expertise. Key Responsibilities • Partner closely with Data Science, Data Engineering, and Software Engineering teams to operationalize machine learning solutions. • Design, build, deploy, and maintain scalable ML systems and production-grade AI applications. • Serve as the bridge between model development, infrastructure, and deployment. • Develop and implement proof of concepts (POCs) and contribute directly to project execution. • Define solution architecture and recommend appropriate cloud services, tools, and deployment approaches. • Design, build, and optimize MLOps workflows, CI/CD pipelines, and monitoring frameworks. • Support modernization initiatives, including migration of legacy platforms to cloud-native architectures. • Contribute to the development of next-generation AI/ML solutions that deliver analytics, predictions, and explainability. • Collaborate on data processing pipelines and engineering workflows that support machine learning applications. Required Skills & Experience Machine Learning Engineering & MLOps • Strong experience with the end-to-end machine learning lifecycle, including: • Model deployment • Monitoring and observability • Model maintenance and retraining • MLOps best practices • Experience deploying and managing ML models in production environments. • Understanding of AI systems, model integration, and AI agent deployment. Cloud Platforms • Hands-on experience with one or more major cloud platforms such as AWS or GCP. • Experience with cloud-based ML services and containerized deployments. • Familiarity with services related to model training, orchestration, deployment, monitoring, and scaling. • Experience implementing CI/CD pipelines and deployment automation. Data Engineering • Experience building and supporting data pipelines and data processing frameworks. • Understanding of data integration, transformation, and workflow orchestration. • Familiarity with DBT is preferred but not mandatory. Architecture & System Design • Ability to design scalable, secure, and maintainable ML solutions. • Experience making infrastructure and architecture decisions for AI/ML workloads. • Strong problem-solving and technical design capabilities. Preferred Technology Stack • Cloud Platforms: AWS, GCP • Data Platform: Snowflake (including AI capabilities such as Cortex) • Application Layer: Streamlit • Containerization and Deployment Frameworks • CI/CD and MLOps Tooling • Modern Data Engineering and ML Deployment Frameworks Experience Required • 6 years+ of relevant experience in Machine Learning Engineering, DevOps, MLOps, Data Engineering, or related fields. • Demonstrated experience building, deploying, and supporting ML applications in production environments. • Strong balance of technical architecture knowledge and hands-on implementation experience. CALL SYNOPSIS- What the performer will be doing (this can be shared with candidates): • Be embedded within the Data Science team. • Own and build AWS services for the team, including SageMaker, SageMaker ML Pipelines, ECR, ECS, ALB, Secrets Manager, Security Groups, Target Groups, and observability. The individual will also be expected to automate deployments through CI/CD to ensure long-term sustainability, working directly and closely with the Director. • Support new capability enablement initiatives, such as implementing and configuring Superset, Hermes Agent, and developing APIs. The candidate should be comfortable taking ownership of these software-focused initiatives and bringing them to life. • Contribute alongside the Data Science team to build a web platform for the Industry Intelligence Hub, consolidating data from multiple systems, exposing data, generating automated data-driven insights, and enabling embedded chat/agent functionality for data-related queries. • Help build data pipelines using SQL and DBT, while also contributing to the UI using Python.