MLOps engineer
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 FrameworksExperience Required
- 5+ years of relevant experience in Machine Learning Engineering, 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.Important Note
This is a hands-on engineering role rather than a pure architecture position. The ideal candidate should be comfortable contributing directly to solution development, deployments, experimentation, and execution. Profiles with only high-level architecture experience and limited implementation exposure may not be a fit.
Project Scope
The selected candidate will:
- Work on new AI/ML platform development initiatives.
- Support modernization and migration of existing systems.
- Contribute to building intelligent systems focused on analytics, prediction, automation, and explainability.
- Help establish scalable engineering practices for AI and machine learning adoption across the organization.