ML Platform Engineer

Responsibilities

• Platform Development: Design, build, and maintain scalable machine learning platforms to support model development, experimentation, and production workflows.
• Infrastructure Automation: Automate the deployment and scaling of ML infrastructure, including data pipelines, model training, validation, and deployment.
• Model Lifecycle Management: Manage the end-to-end lifecycle of machine learning models, including versioning, deployment, monitoring, and retraining.
• LLM Operations (LLM Ops): Implement systems and practices for managing large language models (LLMs), ensuring efficient fine-tuning, deployment, and monitoring of these models in production.
• Collaboration with Data Scientists and Engineers: Provide infrastructure and tools that enable seamless collaboration between data science teams and engineering for the development and deployment of machine learning models.
• Performance Optimization: Optimize model inference and training performance on a range of hardware architectures, including GPU and cloud-based environments.
• Security and Compliance: Ensure the security of the ML platform and compliance with relevant regulations and standards, especially in environments dealing with sensitive data.
• Tooling and Frameworks: Evaluate and integrate MLOps tools, frameworks, and libraries to continuously improve platform capabilities and efficiency.
• Monitoring and Alerting: Implement robust monitoring and alerting systems for production models, ensuring reliability and timely detection of performance drift or anomalies.
• User-Centric Development: Emphasize user needs and experiences in platform design and implementation.
• Adaptive Problem-Solving: Quickly adapt to changing requirements and technological landscapes in ML and AI.
• Product Focus: Maintain a strong product-oriented mindset, aligning technical solutions with business goals and user needs.

Skills and Experience required

• Experience:
  • 3+ years of experience in software engineering or infrastructure roles, with a focus on machine learning platforms or MLOps.
  • Proven experience in building, deploying, and maintaining ML platforms or systems at scale.
  • Strong experience with cloud platforms such as AWS, GCP, or Azure, particularly for machine learning and data processing tasks.
  • Experience with containerization technologies (Docker) and orchestration tools (Kubernetes) for ML workloads.
  • Proficiency in programming languages such as Python, and familiarity with ML libraries and frameworks (e.g., TensorFlow, PyTorch).
  • Familiarity with CI/CD pipelines tailored for machine learning (e.g., model validation, deployment automation).
• Technical Expertise:
  • Experience with distributed systems, model serving, and scaling ML models in production.
  • Hands-on experience with MLOps tools and frameworks such as MLflow, Kubeflow, or similar.
  • Strong understanding of model monitoring, performance optimization, and retraining strategies.
  • Exposure to LLM Ops, including fine-tuning, deploying, and maintaining large language models.
  • Strong focus on automation and experience with infrastructure-as-code tools such as Terraform or CloudFormation.
  • Strong problem-solving skills and experience troubleshooting infrastructure and platform issues

  • Key Attributes:

  • Ability to thrive in fast-paced environments and deliver with high velocity
  • Strong product focus and ability to empathize with end-users of ML platforms
  • Adaptability to rapidly changing ML landscapes and emerging technologies
  • Excellent communication skills to bridge gaps between technical and non-technical stakeholders

  • Preferred Qualifications:

  • Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field
  • Experience with managing the infrastructure for large language models (LLMs) and their specialized operational needs.
  • Experience with big data processing frameworks like Apache Spark, Kafka, or similar.

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