Senior Machine Learning Engineer
2 weeks ago
As a Senior Machine Learning Engineer, you will be responsible for designing and developing machine learning platforms that scale across large production environments. You’ll play a critical role in shaping and implementing software engineering standards, mentoring a high-performing team, and ensuring the effective integration of machine learning models within the company’s cloud infrastructure. Your expertise will contribute to all aspects of the platform lifecycle, from ideation to deployment, with a particular focus on architecture, automation, and performance.
Key Responsibilities:
- Develop scalable software solutions that adhere to the company’s engineering standards.
- Contribute to all stages of the platform lifecycle with a primary focus on software engineering and machine learning.
- Lead and promote best practices within an agile development environment to meet business needs and improve time to market.
- Collaborate closely with the engineering manager to onboard, coach, and mentor engineers, ensuring a high-performing team and smooth transfer of competence.
- Design, define, and educate other engineers on effective practices in machine learning platform development, including model deployment and observability.
- Define and maintain solution and landscape architecture, ensuring scalability and reliability.
- Continuously improve integration and delivery pipelines for machine learning solutions.
- Explore and integrate new technologies to enhance machine learning platforms and product offerings.
- Take ownership of products from ideation through to runtime, including potential on-call duties.
- Nurture skills in system architecture, infrastructure management, platform engineering, and software development.
Required Qualifications:
- Experience developing tools, frameworks, and custom components for machine learning platforms (e.g., model training, deployment, observability, versioning, explainability, feature store).
- Strong background in designing and maintaining large-scale data and cloud infrastructures (GCP/Azure) for machine learning.
- Proven experience with CI/CD workflows and modern DevOps practices aimed at automating deployment pipelines.
- Expertise in software engineering practices to create efficient and scalable ML solutions for critical production environments.
- In-depth knowledge of Azure and GCP for deploying AI/ML solutions.
- Familiarity with Large Language Models and frameworks like Nvidia Nemo, TensorRT, K-Serve, K-Native.
- Hands-on experience with open-source technologies such as MongoDB, Apache Kafka, and others.
- Strong foundation in DevOps principles, including experience with modern infrastructure automation and orchestration tools.
Preferred Qualifications:
- Prior experience with Machine Learning Ops (MLOps).
- Familiarity with security practices for cloud-based machine learning models.
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