Senior AI/ML Architect

Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.

EchoStar requires an enterprise architecture vision to enable AI-driven digital transformation across network, OSS/BSS, and cloud-native environments. This role bridges business strategy and technology execution by designing target-state frameworks, rationalizing technology stacks, and establishing governance standards.

Solving key operational challenges involves embedding scalable machine learning solutions, real-time analytics, and automated decision-making into complex telecommunications infrastructure.

- Lead the enterprise AI architecture strategy to align digital transformation goals with measurable business outcomes across telecom systems

- Architect, train, and deploy production machine learning models for network optimization, predictive maintenance, and operational analytics

- Integrate AI/ML capability models into legacy and modern OSS/BSS platforms to automate high-volume decision-making

- Build resilient real-time streaming frameworks using cloud-native and edge computing principles for high-throughput network insights

- Establish robust governance, model lifecycle management, and ethical compliance standards to scale AI operations safely

- Guide cross-functional teams and junior engineers to accelerate experimentation and maintain technical alignment

- Deep technical expertise in enterprise AI architecture, including proficiency with platforms such as AWS Bedrock, Google Vertex, Databricks, or IBM Watson and core ML frameworks

- Critical experience architecting and delivering large-scale AI solutions within telecommunications, mobile network, or cloud-native environments

- Advanced AI Application and Innovation capability to translate business requirements into scalable technology blueprints and automated systems

- Strong proficiency in real-time data streaming architectures, big data infrastructure (Kafka, Spark, Hadoop), and OSS/BSS system integration

- Demonstrated governance expertise in model lifecycle management, version control, explainability, and AI privacy frameworks

- Proven leadership skills in executive stakeholder management, cross-functional collaboration, and technical mentorship

- Hands-on experience with AI-powered network tuning, network slicing, and intent-based networking

- Familiarity with advanced AI techniques, including deep reinforcement learning, federated learning, and model explainability

- Knowledge of AI ethics, regulatory compliance in telecom, and data privacy frameworks

- Minimum Education: Bachelor's Degree in Computer Science, Engineering, Business, or a related technical discipline (Master's preferred)

- Minimum Experience: 12+ years of enterprise architecture experience, with demonstrated experience in telecommunications or mobile network operators

- AI/ML platforms and frameworks (TensorFlow, PyTorch, Scikit-learn, AWS Bedrock, Google Vertex, Databricks, IBM Watson)

- Big data processing tools (Kafka, Spark, Hadoop) and OSS/BSS systems