Director of AI and ML Engineering - Systems Integrator

Hamilton Barnes
Broomfield, CO, United States
about 2 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$220,000.0
Working hours
Regular working hours

Tech stack

Artificial Intelligence Amazon Web Services Microsoft Azure Cloud Engineering Continuous Integration Data Centers Machine Learning Tensorflow Pytorch Large Language Models Information Technology Low Latency
+1 more
Machine Learning Operations

Job description

Join a leading IT solutions provider delivering security, cloud, networking, data center, and managed services to organizations worldwide. With a customer-first approach and deep technical expertise, the organization helps businesses modernize, optimize, and scale their IT infrastructure. This opportunity is for a Director of AI & ML Engineering to lead the development, delivery, and operationalization of AI and machine learning capabilities across software products and internal platforms. The role involves building and mentoring an AI/ML engineering team, collaborating with cross-functional stakeholders, and driving AI initiatives from strategy and deployment through ongoing governance, optimization, and lifecycle management. Ready to make a move? Get in touch and apply today! Responsibilities:

  • Execute the company’s AI strategy, aligning with Customer’s business objectives and the evolving needs of the satellite connectivity industry
  • Lead the design, build, and deployment of AI/ML solutions that improve customer experience and operational outcomes, including areas such as network/service insights, predictive maintenance, anomaly detection, support automation, and sales enablement
  • Own the end-to-end delivery lifecycle for AI/ML initiatives: problem framing, data readiness, experimentation, production-ization, monitoring, and continuous improvement
  • Partner with Product to translate business goals into actionable AI/ML roadmaps and measurable outcomes tied to customer and operational value
  • Establish and mature ML Ops and LLM Ops practices: model versioning, CI/CD, evaluation, monitoring, drift detection, retraining workflows, and production support
  • Define engineering standards for AI/ML systems including quality, reliability, security, latency, cost-to-serve, and scalability

Requirements

Skills/Must Have:

  • Demonstrated success taking AI/ML systems into production and owning operational performance (monitoring, reliability, retraining, cost)
  • Strong experience with modern ML tooling and frameworks (e.g., PyTorch, TensorFlow) and cloud-based AI services (Azure, AWS, or GCP)
  • Proven ability to lead cross-functional execution and communicate effectively with engineering, product, and business stakeholders
  • Strong understanding of data privacy, security, and governance practices in production systems
  • Experience delivering AI/ML capabilities in connectivity, telecommunications, aviation, or other high-reliability industries

Benefits & conditions

  • $220,000

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Good distractions

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