Informacje o stanowisku

technologies-expected :

• Python
• Linux

• AWS
• Terraform
• OpenTelemetry
• Prometheus
• Grafana

• Machine Learning System (MLOps) Engineers will focus on the technical work required to design, implement, and optimize systems, processes, and pipelines that support the lifecycle and operationalization of machine learning products.

• Unlike data scientists who prioritize understanding data and building accurate models, and ML engineers that focus on systems, processes and pipelines that enable the models to operate effectively, MLOps Engineers ensure these models and piplines are seamlessly integrated into scalable and reliable production systems. This role is not DevOps in AI; it involves solving complex software engineering challenges, automating workflows, and collaborating as equals with data scientists and other machine learning engineers. The distinguishing factor for an MLOps Engineer is their expertise in bridging the gap between model development and deployment, leveraging domain knowledge of machine learning frameworks, cloud infrastructure, and tools for observability and orchestration to drive system performance and reliability

• Must have:
• Programming/Tech Environments: Mastery in Python, Linux, and designing scalable distributed systems.
• MLOps Leadership: Demonstrated expertise in leading CI/CD, containerization, orchestration, and pipeline development.
• Engineering Approach: Follow best practices in modern software engineering and MLOps tooling, applying them to build robust, scalable MLOps systems.
• Highly Desirable:
• Cloud Computing: Comprehensive expertise in AWS, Terraform, and managing distributed infrastructure at scale.
• Observability Leadership: Strategic deployment of tools like OpenTelemetry, Prometheus, and Grafana across production systems.
• LLM and Generative AI: Advanced development and lifecycle management of LLMs, focusing on scalability and safeguards
• Scaling Systems: Extensive experience in architecting systems that handle massive datasets and multi-node processing.
• Qualifications:
• Proven expertise in MLOps, with a strong track record of designing, implementing, and scaling robust systems to support advanced machine learning workflows.
• Experience leading cross-functional initiatives that streamline collaboration between data scientists and ML engineers, boosting productivity and operational efficiency.
• Strategic leadership in the development and maintenance of LLM and generative AI systems, with a focus on scalability, security, and performance.
• Deep knowledge of observability and monitoring practices, ensuring high availability and reliability of production ML systems.
• Skilled in managing complex, interdependent projects, balancing business priorities with technical excellence.
• Recognized mentor to senior engineers; actively shapes the engineering culture, strategic direction, and best practiceswithin MLOps teams.
• Embodies the principles of the Nearmap Leadership DNA, including strategic thinking, innovation, accountability, and team empowerment.
• Acts with integrity and professionalism, adhering to company values, corporate code of conduct, and relevant legislative and compliance frameworks.

• Access to LinkedIn Learning.
• Nearmap subscription.
• Wellbeing and technology allowance.

• remote work opportunities
• flexible working time

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