Senior AI​/ML Architect

Job Description Summary

Senior AI/ML Architect is a visionary leader responsible for defining and delivering scalable, innovative AI solutions for Gas Power Controls. This role entails architecting systems that leverage advanced AI to solve complex business problems and enable transformative applications. You will lead development of products supporting power producing customers and support enterprise-scale AI initiatives leveraging Bedrock foundational models, Azure OpenAI, Google Gemini, and Open Weight models.

The core platform is based on AWS, with additional integrations into Azure for specific AI use cases. The Senior AI/ML Architect works closely with product owners, data scientists, and software development teams to design frameworks, deploy applications, and ensure seamless integration with enterprise systems. As a technical authority, this role emphasizes system scalability, high performance, security, and ethical considerations. You will guide teams in adopting generative AI technologies, mentor engineering teams, and drive innovation to deliver a competitive edge.

• Architect and oversee the development of robust, scalable systems using generative AI models.
• Collaborate with stakeholders to define business requirements and technical specifications for generative AI applications.
• Guide the selection, customization, and optimization of state-of-the-art generative AI models.
• Design a system to maintain deployed solutions at customer sites.
• Develop end-to-end pipelines for inference and monitoring in production environments.
• Ensure systems meet high standards for performance, scalability, and security while adhering to data privacy regulations.
• Lead the implementation of APIs, microservices, and frameworks to integrate AI models into enterprise solutions.
• Design scalable and efficient architectures for generative AI models, applications, and workflows.
• Ensure seamless integration of Generative AI capabilities into existing systems.
• Provide mentorship to engineering teams, fostering expertise in AI and software architecture.
• Design and maintain platforms to handle large-scale solution applications in collaboration with legal/compliance teams.
• Define architectural best practices to mitigate risks associated with Generative AI (e.g., model hallucinations).
• Align AI architecture with organizational goals and contribute to strategic technology roadmaps.
• Document architectural designs, workflows, and decisions for transparency and scalability.

• Bachelor’s degree or higher in a relevant discipline.
• 8+ years of experience within software engineering or a related field.
• Authorized to work in the United States; sponsorship is not supported for this role.

• Deep understanding of LLM integration patterns (RAG, Agents, Tool-use) and Prompt Engineering strategies.
• Expertise in designing scalable, distributed architectures for AI systems.
• Strong experience with cloud computing platforms (AWS, Azure, GCP) and containerization (Kubernetes, Docker).
• Knowledge of on-prem/disconnected deployments, containerization on bare metal, and hardware constraints.
• Familiarity with large-scale distributed systems and database technologies.
• Experience in creating technical design documents and implementation playbooks for target-state AI solutions within cloud environments.
• Experience translating business requirements into technical solution designs.
• Thorough understanding of integration platforms and protocols (e.g., REST, SOAP, HTTP, UDP).
• Proficiency in designing RESTful APIs and Graph
  
  QL endpoints for AI services.
• Knowledge of API development, microservices architecture, and Dev Ops practices.
• Proficiency in MLOps/LLMOps and model lifecycle management, including CI/CD pipelines for training, testing, and deploying AI models at scale.
• Performance optimization for AI/ML workloads, including GPU/TPU acceleration, model quantization, pruning, and distillation.
• Observability & Monitoring of AI pipelines, encompassing logging, tracing, and metrics to detect drift, anomalies, or performance bottlenecks.
• Security, Privacy, and Compliance knowledge, with an understanding of data governance (GDPR, HIPAA, SOC
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