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Senior machine learning engineer
1 week ago
RESPONSABILITIES :
Lead the implementation of a cutting-edge ML infrastructure, encompassing all facets from model inception to deployment, ensuring adherence to best practices and high overall quality standards.
Provide exceptional technical leadership, mentoring, and guidance to a team of machine learning engineers, fostering a culture of continuous learning and innovation.
Take ownership of critical projects and initiatives, providing project leadership, and ensuring successful delivery through effective project management and communication. Engage with stakeholders across the group, understanding their needs, working through the complexity, and conflicting goals.
Collaborate closely with data scientists to translate intricate model requirements into optimized data pipelines, ensuring impeccable data quality, processing, and integration.
Spearhead the establishment of best practices for model versioning, experiment tracking, and model evaluation to ensure transparency and reproducibility.
Architect and execute model deployment strategies, harnessing containerization (Docker) and orchestration (Kubernetes) for exceptional scalability and reliability.
Engineer automated CI/CD pipelines that facilitate seamless model deployment, monitoring, and continuous optimization.
Define and refine performance benchmarks, and optimize models and infrastructure to achieve peak efficiency, scalability, and robustness.
Remain at the forefront of industry trends and emerging technologies, expertly integrating the latest advancements into our ML ecosystem.
QUALIFICATIONS :
Extensive experience orchestrating the development of end-to-end machine learning infrastructure for intricate and large-scale applications.
Proven record of transformative leadership, guiding technical teams to achieve remarkable outcomes and innovation.
Proven track record of delivering sophisticated ML solutions with high quality.
Exceptional problem-solving and analytical skills, with a passion for tackling complex technical and business problems. challenges.
Solid understanding of data structures, algorithms, and software design principles.
Profound mastery of machine learning frameworks such as Tensor Flow, Py Torch, or equivalent, coupled with Python programming.
Deep expertise in containerization (Docker) and orchestration (Kubernetes) for orchestrating complex machine learning applications.
Thorough comprehension of software engineering principles, version control (Git), and collaborative development workflows.
Adeptness with cloud platforms (AWS or Azure) and utilization of cloud-native services for crafting robust ML infrastructure.
Track record of successfully integrating Dev Ops practices, continuous integration, and continuous deployment (CI/CD) pipelines.
Excellent communication and interpersonal skills, with the ability to collaborate effectively in a team-oriented environment.
