Product and AI Platform Engineering Manager

3 weeks ago

Pleasanton, United States Critical River, Inc. Full time

Position Title: Product and AI Platform Engineering Manager

Location: Pleasanton CA (Hybrid)

Employment Type: Full-time

Overview:

We are seeking a highly experienced Product and AI Platform Engineering Leader with deep expertise in MLOps, DataOps, and AI product development. This role will be pivotal in leading the engineering teams to develop scalable, AI-driven products and the infrastructure to support them. The ideal candidate will combine strong leadership, technical expertise in AI platforms, and hands-on experience in deploying MLOps and DataOps best practices to ensure the seamless integration of AI solutions into the business.

Key Responsibilities:

  • Leadership & Strategy:
    • Lead and manage cross-functional engineering teams to develop AI-powered products and the underlying AI platform infrastructure.
    • Define and drive the technical strategy for MLOps and DataOps, ensuring efficient development, deployment, and scaling of AI models and data pipelines.
    • Align platform capabilities with product requirements, business objectives, and future growth needs.
  • MLOps & DataOps Excellence:
    • Establish and scale MLOps practices to streamline the AI model lifecycle, from data collection and model development to deployment, monitoring, and continuous integration/continuous delivery (CI/CD).
    • Lead the design and implementation of robust DataOps pipelines, ensuring smooth data ingestion, processing, and integration for AI models.
    • Implement automation for model training, validation, deployment, and monitoring to improve scalability and operational efficiency.
    • Ensure that MLOps workflows support version control, model governance, reproducibility, and auditability.
  • AI Platform Development:
    • Oversee the development and optimization of a scalable AI platform that supports real-time data processing, model training, deployment, and monitoring across products.
    • Architect and build highly available, distributed systems that handle large-scale data processing and AI model execution.
    • Drive innovation in platform capabilities, ensuring it can support various AI products and services while optimizing for performance, security, and reliability.
  • Product Engineering:
    • Collaborate with product managers, UX/UI designers, and business stakeholders to define and prioritize product requirements and technical roadmaps.
    • Lead the end-to-end development of AI-driven products, ensuring timely delivery, high quality, and scalability.
    • Manage the integration of AI models into the product ecosystem, ensuring seamless collaboration between platform, engineering, and data science teams.
  • Cross-Functional Collaboration:
    • Work closely with data scientists, machine learning engineers, and DevOps teams to ensure smooth integration of AI models into the production environment.
    • Collaborate with business stakeholders and executive leadership to ensure AI solutions meet business needs and deliver tangible value.
    • Foster a collaborative and innovative culture across the teams, encouraging knowledge sharing and continuous learning.
  • Technology & Innovation:
    • Stay ahead of the latest trends in AI, MLOps, DataOps, and cloud computing to ensure the platform remains at the cutting edge of innovation.
    • Evaluate new tools, frameworks, and methodologies to continuously improve the AI product development and deployment processes.
    • Lead efforts to optimize platform performance, data pipeline efficiency, and the scalability of AI models.
  • Operational Efficiency:
    • Establish metrics for measuring the success and performance of AI products and platforms, ensuring continuous monitoring, optimization, and improvement.
    • Address technical challenges, bottlenecks, and risks in the platform and product development process.
    • Ensure compliance with data privacy regulations, security standards, and ethical AI guidelines in all AI deployments.

Required Qualifications:

  • Experience:
    • 10+ years in software engineering, with 5+ years in AI platform and product development roles.
    • Proven experience leading MLOps and DataOps initiatives at scale, including the automation of AI model lifecycle management.
  • Technical Expertise:
    • Strong experience in AI/ML frameworks (e.g., TensorFlow, PyTorch), cloud platforms (AWS, Azure, Google Cloud), and MLOps tools (Kubeflow, MLflow).
    • Deep understanding of for AI models, automation of model training, validation, and deployment.
    • Proficiency in DataOps, including experience with large-scale data processing technologies (Spark, Kafka, Hadoop) and data pipeline orchestration tools (Apache Airflow, Prefect).
    • Strong knowledge of programming languages such as Python, Java, or Scala.
  • Leadership:
    • Proven ability to lead and mentor high-performing engineering teams, with a focus on collaboration and innovation.
    • Ability to align platform development with business strategy and manage cross-functional teams in a fast-paced environment.
  • Communication:
    • Strong ability to translate complex technical concepts into clear communication for stakeholders at all levels, from engineers to executives.
  • Education:
    • Bachelor s or Master s degree in Computer Science, Engineering, or related field.
    • Advanced degree or certifications in AI, MLOps, or DataOps preferred.

Preferred Qualifications:

  • Experience in AI product deployment within industries such as finance, healthcare, or manufacturing.
  • Familiarity with AI governance, model explainability, and ethical AI principles.
  • Experience with containerization and orchestration technologies (e.g., Kubernetes, Docker) for AI model deployment.

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