Lead Software Engineer - MLOps Engineer

Job Description: ML OPS Engineer (LEAD)

You will be responsible for building, operating, and evolving the Data Science Delivery Platform (DSDP), enabling Data Scientists and Engineers to develop, deploy, and scale AI/ML solutions securely and efficiently. You will work closely with Data Science teams, infrastructure teams, and business stakeholders to maintain and evolve a reliable, self-service AI platform.

Key Responsibilities

• Build and maintain scalable platform services on Kubernetes.
• Build and maintain tools to facilitate the end-to-end data science lifecycle, covering experimentation, deployment, monitoring, and governance.
• Support and enhance enterprise AI tools such as Dataiku, MLFlow, Snowflake, and Spark.
• Implement automation, CI/CD pipelines, and platform engineering best practices.
• Ensure platform reliability, observability, security, and compliance.
• Provide technical support and enablement for Data Scientists and Data Engineers.
• Continuously improve platform capabilities and user experience.

Technical Environment

• Infrastructure: Kubernetes, Docker, Linux
• AI/ML: Dataiku, MLFlow, Kedro, Jupyter Notebooks
• Data: Snowflake, PostgreSQL, Spark, Hadoop
• Development: Python, FastAPI, SQLAlchemy, PyTest
• DevOps: GitHub Actions, Jenkins, Ansible, Terraform, Harbor, JFrog
• Observability: Grafana, Kibana, Elasticsearch, Zabbix

Required Skills

• Strong hands-on experience with Kubernetes and container technologies; debugging platform issues and operational anomalies in Kubernetes environments is a core day-to-day responsibility
• Strong server administration expertise across Linux environments, system operations, networking, performance troubleshooting, access management, and infrastructure reliability
• Practical Python development skills for platform automation, operational tooling, integration, and troubleshooting activities
• Hands-on experience with Terraform for infrastructure provisioning, configuration management, and infrastructure-as-code automation
• Experience building and maintaining CI/CD pipelines using GitHub Actions, Ansible, Jenkins, and related platform automation frameworks
• Good understanding of MLOps practices, AI/ML lifecycle management, model deployment, and Data Science workflows
• Knowledge of monitoring, observability, troubleshooting, and platform operations to ensure service reliability and operational continuity
• Experience with Dataiku, MLflow, or similar AI/ML platforms is required

Soft Skills

• Customer-focused and collaborative mindset - You will have to interact with data scientists and support them with platform issues on a day-to-day basis.
• Strong ownership and problem-solving abilities.
• Ability to communicate effectively with technical and non-technical stakeholders.
• A drive for innovation, simplification, and continuous improvement.

Preferred Experience

• 8 years in Platform Engineering and MLOps Engineering.
• Experience supporting enterprise AI/ML platforms at scale.
• Exposure to GenAI/LLMOps technologies is a plus.