Sr Machine Learning Engineer – Mlops

Sr Machine Learning Engineer – Mlops

Requirements

• Experience in machine learning engineering or related roles, with a track record of developing and deploying models in production environments.
• Familiarity with cloud computing platforms (AWS, Azure, GCP – Preferred) and containerization technologies (Docker, Kubernetes).
• Excellent problem-solving skills with attention to detail, effective communication, and collaboration skills across technical and non-technical teams.
• Excellent written and verbal communication skills
• Proven experience in implementing and managing CI/CD pipelines, including vulnerability scanning, unit testing, automated deployment using tools such as ADO, Jenkins, JFrog, SonarQube.
• Strong understanding of the full ML lifecycle, including data pipelining, model training, testing, deployment, and monitoring.
• Experience designing, developing, and deploying APIs to host models using frameworks like Flask, FastAPI, etc.
• Comfortable using Git, Powershell, Bash.
• Strong knowledge of cloud infrastructure and services used for supporting model deployment (Cloud Storage, Databricks, Airflow/Kubeflow, Docker, Kubernetes, Vertex, BigQuery, etc.).
• Leverages infrastructure as code tools (i.e. Terraform & Helm).
• Bachelor’s degree in computer science, engineering, mathematics, or related field.

Responsibilities

• Researches and implements appropriate ML algorithms and tools that create new systems and processes powered with ML and AI tools and techniques according to business requirements.
• Establishes, configures, and supports scalable cloud components that serve predictive model transactions.
• Collaborates with skilled Designers, Architects, Software Engineers, Data Scientists and Data Engineers to deliver ML products and systems for the organization.
• Design, implement, and manage CI/CD pipelines to streamline the deployment of machine learning models into production environments.
• Transition data science ML prototypes into reliable production-grade solutions. Build and maintain APIs to host machine learning models ensuring smooth integration with other applications while prioritizing scalability, security, and maintainability.
• Establish and enforce best practices for version control of solutions, including application & model versioning, ensuring traceability and reproducibility for troubleshooting.
• Integrates data from authoritative internal and external sources with data pipelines, ensuring that sensitive data is handled appropriately and efficiently, adhering to best security practices.
• Implement monitoring systems for tracking model and API performance in production with the ability to identify anomalies and report important business statistics.
• Automate repetitive tasks such as model deployment and scalability of model features, while ensuring appropriate uptime to meet business RTOs & RPOs.
• Document system designs & workflows to ensure transparency and maintainability of infrastructure.

Preferred Qualifications

• Experience in Agile/Scrum methodologies and interdisciplinary team environments is a plus.